A Collaborative Data Science Development Workflow
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Collaborative data science workflows have several moving parts, and many organizations struggle with developing an efficient and scalable process. Our solution consists of data scientists individually building and testing Kedro pipelines and measuring performance using MLflow tracking. Once a strong solution is created, the candidate pipeline is trained on cloud-agnostic, GPU-enabled containers. If this pipeline is production worthy, the resulting model is served to a production application through MLflow.
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MLFlow: Platform for Complete Machine Learning Lifecycle
Description
Data Science and ML development bring many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work.
MLflow addresses some of these challenges during an ML model development cycle.
Abstract
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.
In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
With a short demo, you see a complete ML model life-cycle example, you will walk away with: MLflow concepts and abstractions for models, experiments, and projects How to get started with MLFlow Using tracking Python APIs during model training Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
MLOps Using MLflow
MLflow is an MLOps tool that enables data scientist to quickly productionize their Machine Learning projects. To achieve this, MLFlow has four major components which are Tracking, Projects, Models, and Registry. MLflow lets you train, reuse, and deploy models with any library and package them into reproducible steps. MLflow is designed to work with any machine learning library and require minimal changes to integrate into an existing codebase. In this session, we will cover the common pain points of machine learning developers such as tracking experiments, reproducibility, deployment tool and model versioning. Ready to get your hands dirty by doing quick ML project using mlflow and release to production to understand the ML-Ops lifecycle.
Productionalizing Models through CI/CD Design with MLflow
Often times model deployment and integration consists of several moving parts that require intricate steps woven together. Automating this pipeline and feedback loop can be incredibly challenging, especially in lieu of varying model development techniques.
Introduction to MLflow
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure. In this talk, I present MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
Apache Spark Data Validation
In our experience, many problems with production workflows can be traced back to unexpected values in the input data. In a complex pipeline, it can be difficult and costly to trace the root cause of errors. Here we outline our work developing an open source data validation framework built on Apache Spark. Our goal is a tool that easily integrates into existing workflows to automatically make data validation a vital initial step of every production workflow. Our tool is aimed at data scientists and data engineers, who are not necessarily Scala/Python programmers. Our users specify a configuration file that details the data validation checks to be completed. This configuration file is parsed into appropriate queries that are executed with Apache Spark. A status report is logged, which is used to notify developers/maintainers and to establish a historical record of validator checks. This work was inspired by the many great ideas behind Google's TensorFlow Extended (TFX) platform, in particular TensorFlow Data Validation (TFDV). As such we provide optional functionality for our users to visualize their data using Facets Overview and Facets Dive.
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Specialized tools for machine learning development and model governance are becoming essential. MlFlow is an open source platform for managing the machine learning lifecycle. Just by adding a few lines of code in the function or script that trains their model, data scientists can log parameters, metrics, artifacts (plots, miscellaneous files, etc.) and a deployable packaging of the ML model. Every time that function or script is run, the results will be logged automatically as a byproduct of those lines of code being added, even if the party doing the training run makes no special effort to record the results. MLflow application programming interfaces (APIs) are available for the Python, R and Java programming languages, and MLflow sports a language-agnostic REST API as well. Over a relatively short time period, MLflow has garnered more than 3,300 stars on GitHub , almost 500,000 monthly downloads and 80 contributors from more than 40 companies. Most significantly, more than 200 companies are now using MLflow. We will demo MlFlow Tracking , Project and Model components with Azure Machine Learning (AML) Services and show you how easy it is to get started with MlFlow on-prem or in the cloud.
Intro to AutoML + Hands-on Lab - Erin LeDell, Machine Learning Scientist, H2O.ai
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/42Oo8TOl85I.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
- - -
Abstract:
In recent years, the demand for machine learning experts has outpaced the supply, despite the surge of people entering the field. To address this gap, there have been big strides in the development of user-friendly machine learning software that can be used by non-experts. Although H2O has made it easier for practitioners to train and deploy machine learning models at scale, there is still a fair bit of knowledge and background in data science that is required to produce high-performing machine learning models. Deep Neural Networks in particular, are notoriously difficult for a non-expert to tune properly. In this presentation, we provide an overview of the field of "Automatic Machine Learning" and introduce the new AutoML functionality in H2O. H2O's AutoML provides an easy-to-use interface which automates the process of training a large, comprehensive selection of candidate models and a stacked ensemble model which, in most cases, will be the top performing model in the AutoML Leaderboard. H2O AutoML is available in all the H2O interfaces including the h2o R package, Python module and the Flow web GUI. We will also provide simple code examples to get you started using AutoML.
Erin's Bio:
Erin is a Statistician and Machine Learning Scientist at H2O.ai. She is the main author of H2O Ensemble. Before joining H2O, she was the Principal Data Scientist at Wise.io and Marvin Mobile Security (acquired by Veracode in 2012) and the founder of DataScientific, Inc. Erin received her Ph.D. in Biostatistics with a Designated Emphasis in Computational Science and Engineering from University of California, Berkeley. Her research focuses on ensemble machine learning, learning from imbalanced binary-outcome data, influence curve based variance estimation and statistical computing. She also holds a B.S. and M.A. in Mathematics.
MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ...
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure. In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size. In this deep-dive session, through a complete ML model life-cycle example, you will walk away with:
MLflow concepts and abstractions for models, experiments, and projects
How to get started with MLFlow
Understand aspects of MLflow APIs
Using tracking APIs during model training
Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
Package, save, and deploy an MLflow model
Serve it using MLflow REST API
What’s next and how to contribute
Recommended
MLFlow: Platform for Complete Machine Learning Lifecycle
Description
Data Science and ML development bring many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work.
MLflow addresses some of these challenges during an ML model development cycle.
Abstract
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.
In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
With a short demo, you see a complete ML model life-cycle example, you will walk away with: MLflow concepts and abstractions for models, experiments, and projects How to get started with MLFlow Using tracking Python APIs during model training Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
MLOps Using MLflow
MLflow is an MLOps tool that enables data scientist to quickly productionize their Machine Learning projects. To achieve this, MLFlow has four major components which are Tracking, Projects, Models, and Registry. MLflow lets you train, reuse, and deploy models with any library and package them into reproducible steps. MLflow is designed to work with any machine learning library and require minimal changes to integrate into an existing codebase. In this session, we will cover the common pain points of machine learning developers such as tracking experiments, reproducibility, deployment tool and model versioning. Ready to get your hands dirty by doing quick ML project using mlflow and release to production to understand the ML-Ops lifecycle.
Productionalizing Models through CI/CD Design with MLflow
Often times model deployment and integration consists of several moving parts that require intricate steps woven together. Automating this pipeline and feedback loop can be incredibly challenging, especially in lieu of varying model development techniques.
Introduction to MLflow
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure. In this talk, I present MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
Apache Spark Data Validation
In our experience, many problems with production workflows can be traced back to unexpected values in the input data. In a complex pipeline, it can be difficult and costly to trace the root cause of errors. Here we outline our work developing an open source data validation framework built on Apache Spark. Our goal is a tool that easily integrates into existing workflows to automatically make data validation a vital initial step of every production workflow. Our tool is aimed at data scientists and data engineers, who are not necessarily Scala/Python programmers. Our users specify a configuration file that details the data validation checks to be completed. This configuration file is parsed into appropriate queries that are executed with Apache Spark. A status report is logged, which is used to notify developers/maintainers and to establish a historical record of validator checks. This work was inspired by the many great ideas behind Google's TensorFlow Extended (TFX) platform, in particular TensorFlow Data Validation (TFDV). As such we provide optional functionality for our users to visualize their data using Facets Overview and Facets Dive.
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Specialized tools for machine learning development and model governance are becoming essential. MlFlow is an open source platform for managing the machine learning lifecycle. Just by adding a few lines of code in the function or script that trains their model, data scientists can log parameters, metrics, artifacts (plots, miscellaneous files, etc.) and a deployable packaging of the ML model. Every time that function or script is run, the results will be logged automatically as a byproduct of those lines of code being added, even if the party doing the training run makes no special effort to record the results. MLflow application programming interfaces (APIs) are available for the Python, R and Java programming languages, and MLflow sports a language-agnostic REST API as well. Over a relatively short time period, MLflow has garnered more than 3,300 stars on GitHub , almost 500,000 monthly downloads and 80 contributors from more than 40 companies. Most significantly, more than 200 companies are now using MLflow. We will demo MlFlow Tracking , Project and Model components with Azure Machine Learning (AML) Services and show you how easy it is to get started with MlFlow on-prem or in the cloud.
Intro to AutoML + Hands-on Lab - Erin LeDell, Machine Learning Scientist, H2O.ai
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/42Oo8TOl85I.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
- - -
Abstract:
In recent years, the demand for machine learning experts has outpaced the supply, despite the surge of people entering the field. To address this gap, there have been big strides in the development of user-friendly machine learning software that can be used by non-experts. Although H2O has made it easier for practitioners to train and deploy machine learning models at scale, there is still a fair bit of knowledge and background in data science that is required to produce high-performing machine learning models. Deep Neural Networks in particular, are notoriously difficult for a non-expert to tune properly. In this presentation, we provide an overview of the field of "Automatic Machine Learning" and introduce the new AutoML functionality in H2O. H2O's AutoML provides an easy-to-use interface which automates the process of training a large, comprehensive selection of candidate models and a stacked ensemble model which, in most cases, will be the top performing model in the AutoML Leaderboard. H2O AutoML is available in all the H2O interfaces including the h2o R package, Python module and the Flow web GUI. We will also provide simple code examples to get you started using AutoML.
Erin's Bio:
Erin is a Statistician and Machine Learning Scientist at H2O.ai. She is the main author of H2O Ensemble. Before joining H2O, she was the Principal Data Scientist at Wise.io and Marvin Mobile Security (acquired by Veracode in 2012) and the founder of DataScientific, Inc. Erin received her Ph.D. in Biostatistics with a Designated Emphasis in Computational Science and Engineering from University of California, Berkeley. Her research focuses on ensemble machine learning, learning from imbalanced binary-outcome data, influence curve based variance estimation and statistical computing. She also holds a B.S. and M.A. in Mathematics.
MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ...
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure. In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size. In this deep-dive session, through a complete ML model life-cycle example, you will walk away with:
MLflow concepts and abstractions for models, experiments, and projects
How to get started with MLFlow
Understand aspects of MLflow APIs
Using tracking APIs during model training
Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
Package, save, and deploy an MLflow model
Serve it using MLflow REST API
What’s next and how to contribute
Productionzing ML Model Using MLflow Model Serving
Productionzing ML Models are needs to ensure model integrity while it efficiently replicate runtime environments across servers besides it keep track of how each of our models were created. It helps us better trace the root cause of changes and issues over time as we acquire new data and update our model. We have greater accountability over our models and the results they generate.
MLflow Model Serving delivers cost-effective and on-click deployment of model for real-time inferences. Also the Model Version deployed in the Model Serving can also be conveniently managed with MLflow Model Registry. We will going to cover following topics Deployment, Consumption and Monitoring. For deployment, we will demo the different version deployment and validate the deployment. For consumption, we demo connecting power bi and generate prediction report using ML Model deployed in MLflow serving. Lastly will wrap up with managing the MLflow serving like, access rights and monitoring capabilities.
Bighead: Airbnb’s End-to-End Machine Learning Platform with Krishna Puttaswa...
Airbnb has a wide variety of ML problems ranging from models on traditional structured data to models built on unstructured data such as user reviews, messages and listing images. The ability to build, iterate on, and maintain healthy machine learning models is critical to Airbnb’s success. Many ML Platforms cover data collection, feature engineering, training, deploying, productionalization, and monitoring but few, if any, do all of the above seamlessly.
Bighead aims to tie together various open source and in-house projects to remove incidental complexity from ML workflows. Bighead is built on Python and Spark and can be used in modular pieces as each ML problem presents unique challenges. Through standardization of the path to production, training environments and the methods for collecting and transforming data on Spark, each model is reproducible and iterable.
This talk covers the architecture, the problems that each individual component and the overall system aims to solve, and a vision for the future of machine learning infrastructure. It’s widely adapted in Airbnb and we have variety of models running in production. We have seen the overall model development time go down from many months to days on Bighead. We plan to open source Bighead to allow the wider community to benefit from our work.
Unified MLOps: Feature Stores & Model Deployment
If you’ve brought two or more ML models into production, you know the struggle that comes from managing multiple data sets, feature engineering pipelines, and models. This talk will propose a whole new approach to MLOps that allows you to successfully scale your models, without increasing latency, by merging a database, a feature store, and machine learning.
Splice Machine is a hybrid (HTAP) database built upon HBase and Spark. The database powers a one of a kind single-engine feature store, as well as the deployment of ML models as tables inside the database. A simple JDBC connection means Splice Machine can be used with any model ops environment, such as Databricks.
The HBase side allows us to serve features to deployed ML models, and generate ML predictions, in milliseconds. Our unique Spark engine allows us to generate complex training sets, as well as ML predictions on petabytes of data.
In this talk, Monte will discuss how his experience running the AI lab at NASA, and as CEO of Red Pepper, Blue Martini Software and Rocket Fuel, led him to create Splice Machine. Jack will give a quick demonstration of how it all works.
Fully Utilizing Spark for Data Validation
Data validation is becoming more important as companies have increasingly interconnected data pipelines. Validation serves as a safeguard to prevent existing pipelines from failing without notice. Currently, the most widely adopted data validation framework is Great Expectations. They have support for both Pandas and Spark workflows (with the same API). Great Expectations is a robust data validation library with a lot of features. For example, Great Expectations always keeps track of how many records are failing a validation, and stores examples for failing records. They also profile data after validations and output data documentation.
These features can be very useful, but if a user does not need them, they are expensive to generate. What are the options if we need a more lightweight framework? Pandas has some data validation frameworks that are designed to be lightweight. Pandera is one example. Is it possible to use a lightweight Pandas-based framework on Spark? In this talk, we’ll show how this is possible with a library called Fugue. Fugue is an open-source framework that lets users port native Python code or Pandas code to Spark. We will show an interactive demo of how to extend Pandera (or any other Pandas-based data validation library) to a Spark workflow.
There is also a deficiency in the current frameworks we will address in the demo. With big data, there is a need to apply different validation rules for each partition. For example, data that encompasses a lot of geographic regions may have different acceptable ranges of values (think of currency). Since the current frameworks are designed to apply a validation rule to the whole DataFrame, this can’t be done. Using Fugue and Pandera, we can apply different validation rules on each partition of data.
Ml ops intro session
ML Ops case study document which talks about How ML pipelines are implemented and how to choose and implement Continuous Data 4 ML Pipelines.
Data engineering design patterns
There are patterns for things such as domain-driven design, enterprise architectures, continuous delivery, microservices, and many others.
But where are the data science and data engineering patterns?
Sometimes, data engineering reminds me of cowboy coding - many workarounds, immature technologies and lack of market best practices.
Databricks Overview for MLOps
Databricks Overview for MLOps by Clemend Mewald, Director of Product Management, Databricks
Building a Feature Store around Dataframes and Apache Spark
A Feature Store enables machine learning (ML) features to be registered, discovered, and used as part of ML pipelines, thus making it easier to transform and validate the training data that is fed into machine learning systems. Feature stores can also enable consistent engineering of features between training and inference, but to do so, they need a common data processing platform.
Auto-Train a Time-Series Forecast Model With AML + ADB
Supply Chain, Healthcare, Insurance, and Finance often require highly accurate forecasting models in an enterprise large-scale fashion. With Azure Machine Learning on Azure Databricks, the scale and speed to large-scale many-models can be achieved and time-to-product decreases drastically. The better-together story poses an enterprise approach to AI/ML.
Azure AutoML offers an elegant solution efficiently to build forecasting models on Azure Databricks compute solving sophisticated business problems. The presentation covers the Azure Machine Learning + Azure Databricks approach (see slides attached) while the demo covers a hands-on business problem building a forecasting model in Azure Databricks using Azure Machine Learning. The AI/ML better-together story is elevated as MLFlow for Data Science Lifecycle Management and Hyperopt for distributed model execution completes AI/ML enterprise readiness for industry problems.
MLOps - The Assembly Line of ML
The catalyst for the success of automobiles came not through the invention of the car but rather through the establishment of an innovative assembly line. History shows us that the ability to mass produce and distribute a product is the key to driving adoption of any innovation, and machine learning is no different. MLOps is the assembly line of Machine Learning and in this presentation we will discuss the core capabilities your organization should be focused on to implement a successful MLOps system.
Frame - Feature Management for Productive Machine Learning
Presented at the ML Platforms Meetup at Pinterest HQ in San Francisco on August 16, 2018.
Abstract: At LinkedIn we observed that much of the complexity in our machine learning applications was in their feature preparation workflows. To address this problem, we built Frame, a shared virtual feature store that provides a unified abstraction layer for accessing features by name. Frame removes the need for feature consumers to deal directly with underlying data sources, which are often different across computing environments. By simplifying feature preparation, Frame has made ML applications at LinkedIn easier to build, modify, and understand.
MLflow: Infrastructure for a Complete Machine Learning Life Cycle
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.
In this talk, we will present MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
High-speed Database Throughput Using Apache Arrow Flight SQL
Flight SQL is a revolutionary new open database protocol designed for modern architectures. Key features in Flight SQL include a columnar-oriented design and native support for parallel processing of data partitions. This talk will go over how these new features can push SQL query throughput beyond existing standards such as ODBC.
Building End-to-End Delta Pipelines on GCP
Delta has been powering many production pipelines at scale in the Data and AI space since it has been introduced for the past few years.
Built on open standards, Delta provides data reliability, enhances storage and query performance to support big data use cases (both batch and streaming), fast interactive queries for BI and enabling machine learning. Delta has matured over the past couple of years in both AWS and AZURE and has become the de-facto standard for organizations building their Data and AI pipelines.
In today’s talk, we will explore building end-to-end pipelines on the Google Cloud Platform (GCP). Through presentation, code examples and notebooks, we will build the Delta Pipeline from ingest to consumption using our Delta Bronze-Silver-Gold architecture pattern and show examples of Consuming the delta files using the Big Query Connector.
Introduction to RAG (Retrieval Augmented Generation) and its application
Embark on a comprehensive exploration of Retrieval Augmented Generation (RAG) in this illuminating session. Delve into the architecture seamlessly merging retrieval and generation models and uncover its versatile applications. From refining search processes to enhancing content generation, RAG is reshaping the landscape of natural language processing. Join us for a brief yet comprehensive Introduction to RAG and its transformative potential, along with insights into its applications.
Model serving made easy using Kedro pipelines - Mariusz Strzelecki, GetInData
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Presentation from the performance given by Mariusz during the Data Science Summit ML Edition.
Author: Mariusz Strzelecki
Linkedin: https://www.linkedin.com/in/mariusz-strzelecki/
___
Company:
Getindata is a company founded in 2014 by ex-Spotify data engineers. From day one our focus has been on Big Data projects. We bring together a group of best and most experienced experts in Poland, working with cloud and open-source Big Data technologies to help companies build scalable data architectures and implement advanced analytics over large data sets.
Our experts have vast production experience in implementing Big Data projects for Polish as well as foreign companies including i.a. Spotify, Play, Truecaller, Kcell, Acast, Allegro, ING, Agora, Synerise, StepStone, iZettle and many others from the pharmaceutical, media, finance and FMCG industries.
https://getindata.com
Deploying ML models in the enterprise
The talk was given at OReilly Strata Data Conference September 2018 in NYC
All the conferences and thought leaders have been painting a vision of the businesses of the future being powered by data, but if we’re honest with ourselves, the vast majority of our massive data science investments are being deployed to PowerPoint or maybe a business dashboard. Productionizing your machine learning (ML) portfolio is the next big step on the path to ROI from AI.
You probably started out years ago on a “big data” initiative: You collected and cleaned your data and built data warehouses, and when those filled up you upgraded to data lakes. You hired data engineers and data scientists, and around the organization, everyone brushed up their SQL querying skills and got some licenses to Tableau and PowerBI.
Then you saw what Google, Uber, Facebook, and Amazon were doing with machine learning to automate business processes and customer interactions. To not get broadsided, you hired more data scientists and machine learning engineers. They were put on your teams and started using your big data investments to train models. But what you probably found is that your tech stack and DevOps processes don’t fit ML models. Unlike most of your systems, ML models require short spikes of massive compute; they are often written in different languages than your core code; they need different hardware to perform well; one model probably has applications across many teams; and the people making the models often don’t have the engineering experience to write production code but need to iterate faster than traditional engineers. Expecting your engineering and DevOps teams to deploy ML models well is like showing up to Seaworld with a giraffe since they are already handling large mammals.
There is a path forward. Almost five years ago Algorithmia launched a marketplace for models, functions, and algorithms. Today 65,000 developers are on the platform deploying 4,500 models—the result has been a layer of tools and best practices to make deploying ML models frictionless, scalable, and low maintenance. The company refers to it as the “AI layer.”
Drawing on this experience, Diego Oppenheimer covers the strategic and technical hurdles each company must overcome and the best practices developed while deploying over 4,000 ML models for 70,000 engineers.
Topics include:
Best practices for your organization
Continuous model deployment
Varying languages (Your code base probably isn’t in Python or R, but your ML models probably are.)
Managing your portfolio of ML models
Standardize versioning
Enabling models across your organization
Analytics on how and where models are being used
Maintaining auditability
Getting Started with Databricks SQL Analytics
It has long been said that business intelligence needs a relational warehouse, but that view is changing. With the Lakehouse architecture being shouted from the rooftops, Databricks have released SQL Analytics, an alternative workspace for SQL-savvy users to interact with an analytics-tuned cluster. But how does it work? Where do you start? What does a typical Data Analyst’s user journey look like with the tool?
This session will introduce the new workspace and walk through the various key features – how you set up a SQL Endpoint, the query workspace, creating rich dashboards and connecting up BI tools such as Microsoft Power BI.
If you’re truly trying to create a Lakehouse experience that satisfies your SQL-loving Data Analysts, this is a tool you’ll need to be familiar with and include in your design patterns, and this session will set you on the right path.
MLOps Virtual Event: Automating ML at Scale
MLOps Virtual Event: Automating ML at Scale by Matei Zaharia, Chief Technologist, Databrikcs
Apply MLOps at Scale
This session is continuation of “Automated Production Ready ML at Scale” in last Spark AI Summit at Europe. In this session you will learn about how H&M evolves reference architecture covering entire MLOps stack addressing a few common challenges in AI and Machine learning product, like development efficiency, end to end traceability, speed to production, etc.
[DSC Europe 23] Petar Zecevic - ML in Production on Databricks
Deploying ML models in production, with or without CI/CD, is significantly more complicated than deploying traditional applications. That is mainly because ML models do not just consist of the code used for their training, but they also depend on the data they are trained on and on the supporting code. Monitoring ML models also adds additional complexity beyond what is usually done for traditional applications. This talk will cover these problems and best practices for solving them, with special focus on how it's done on the Databricks platform.
A practical guidance of the enterprise machine learning
This session provides an analysis of the machine learning market in the enterprise. The analysis includes vendors, platforms and best practices that should be considered by companies implementing data science solutions at an enterprise scale
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Productionzing ML Model Using MLflow Model Serving
Productionzing ML Models are needs to ensure model integrity while it efficiently replicate runtime environments across servers besides it keep track of how each of our models were created. It helps us better trace the root cause of changes and issues over time as we acquire new data and update our model. We have greater accountability over our models and the results they generate.
MLflow Model Serving delivers cost-effective and on-click deployment of model for real-time inferences. Also the Model Version deployed in the Model Serving can also be conveniently managed with MLflow Model Registry. We will going to cover following topics Deployment, Consumption and Monitoring. For deployment, we will demo the different version deployment and validate the deployment. For consumption, we demo connecting power bi and generate prediction report using ML Model deployed in MLflow serving. Lastly will wrap up with managing the MLflow serving like, access rights and monitoring capabilities.
Bighead: Airbnb’s End-to-End Machine Learning Platform with Krishna Puttaswa...
Airbnb has a wide variety of ML problems ranging from models on traditional structured data to models built on unstructured data such as user reviews, messages and listing images. The ability to build, iterate on, and maintain healthy machine learning models is critical to Airbnb’s success. Many ML Platforms cover data collection, feature engineering, training, deploying, productionalization, and monitoring but few, if any, do all of the above seamlessly.
Bighead aims to tie together various open source and in-house projects to remove incidental complexity from ML workflows. Bighead is built on Python and Spark and can be used in modular pieces as each ML problem presents unique challenges. Through standardization of the path to production, training environments and the methods for collecting and transforming data on Spark, each model is reproducible and iterable.
This talk covers the architecture, the problems that each individual component and the overall system aims to solve, and a vision for the future of machine learning infrastructure. It’s widely adapted in Airbnb and we have variety of models running in production. We have seen the overall model development time go down from many months to days on Bighead. We plan to open source Bighead to allow the wider community to benefit from our work.
Unified MLOps: Feature Stores & Model Deployment
If you’ve brought two or more ML models into production, you know the struggle that comes from managing multiple data sets, feature engineering pipelines, and models. This talk will propose a whole new approach to MLOps that allows you to successfully scale your models, without increasing latency, by merging a database, a feature store, and machine learning.
Splice Machine is a hybrid (HTAP) database built upon HBase and Spark. The database powers a one of a kind single-engine feature store, as well as the deployment of ML models as tables inside the database. A simple JDBC connection means Splice Machine can be used with any model ops environment, such as Databricks.
The HBase side allows us to serve features to deployed ML models, and generate ML predictions, in milliseconds. Our unique Spark engine allows us to generate complex training sets, as well as ML predictions on petabytes of data.
In this talk, Monte will discuss how his experience running the AI lab at NASA, and as CEO of Red Pepper, Blue Martini Software and Rocket Fuel, led him to create Splice Machine. Jack will give a quick demonstration of how it all works.
Fully Utilizing Spark for Data Validation
Data validation is becoming more important as companies have increasingly interconnected data pipelines. Validation serves as a safeguard to prevent existing pipelines from failing without notice. Currently, the most widely adopted data validation framework is Great Expectations. They have support for both Pandas and Spark workflows (with the same API). Great Expectations is a robust data validation library with a lot of features. For example, Great Expectations always keeps track of how many records are failing a validation, and stores examples for failing records. They also profile data after validations and output data documentation.
These features can be very useful, but if a user does not need them, they are expensive to generate. What are the options if we need a more lightweight framework? Pandas has some data validation frameworks that are designed to be lightweight. Pandera is one example. Is it possible to use a lightweight Pandas-based framework on Spark? In this talk, we’ll show how this is possible with a library called Fugue. Fugue is an open-source framework that lets users port native Python code or Pandas code to Spark. We will show an interactive demo of how to extend Pandera (or any other Pandas-based data validation library) to a Spark workflow.
There is also a deficiency in the current frameworks we will address in the demo. With big data, there is a need to apply different validation rules for each partition. For example, data that encompasses a lot of geographic regions may have different acceptable ranges of values (think of currency). Since the current frameworks are designed to apply a validation rule to the whole DataFrame, this can’t be done. Using Fugue and Pandera, we can apply different validation rules on each partition of data.
Ml ops intro session
ML Ops case study document which talks about How ML pipelines are implemented and how to choose and implement Continuous Data 4 ML Pipelines.
Data engineering design patterns
There are patterns for things such as domain-driven design, enterprise architectures, continuous delivery, microservices, and many others.
But where are the data science and data engineering patterns?
Sometimes, data engineering reminds me of cowboy coding - many workarounds, immature technologies and lack of market best practices.
Databricks Overview for MLOps
Databricks Overview for MLOps by Clemend Mewald, Director of Product Management, Databricks
Building a Feature Store around Dataframes and Apache Spark
A Feature Store enables machine learning (ML) features to be registered, discovered, and used as part of ML pipelines, thus making it easier to transform and validate the training data that is fed into machine learning systems. Feature stores can also enable consistent engineering of features between training and inference, but to do so, they need a common data processing platform.
Auto-Train a Time-Series Forecast Model With AML + ADB
Supply Chain, Healthcare, Insurance, and Finance often require highly accurate forecasting models in an enterprise large-scale fashion. With Azure Machine Learning on Azure Databricks, the scale and speed to large-scale many-models can be achieved and time-to-product decreases drastically. The better-together story poses an enterprise approach to AI/ML.
Azure AutoML offers an elegant solution efficiently to build forecasting models on Azure Databricks compute solving sophisticated business problems. The presentation covers the Azure Machine Learning + Azure Databricks approach (see slides attached) while the demo covers a hands-on business problem building a forecasting model in Azure Databricks using Azure Machine Learning. The AI/ML better-together story is elevated as MLFlow for Data Science Lifecycle Management and Hyperopt for distributed model execution completes AI/ML enterprise readiness for industry problems.
MLOps - The Assembly Line of ML
The catalyst for the success of automobiles came not through the invention of the car but rather through the establishment of an innovative assembly line. History shows us that the ability to mass produce and distribute a product is the key to driving adoption of any innovation, and machine learning is no different. MLOps is the assembly line of Machine Learning and in this presentation we will discuss the core capabilities your organization should be focused on to implement a successful MLOps system.
Frame - Feature Management for Productive Machine Learning
Presented at the ML Platforms Meetup at Pinterest HQ in San Francisco on August 16, 2018.
Abstract: At LinkedIn we observed that much of the complexity in our machine learning applications was in their feature preparation workflows. To address this problem, we built Frame, a shared virtual feature store that provides a unified abstraction layer for accessing features by name. Frame removes the need for feature consumers to deal directly with underlying data sources, which are often different across computing environments. By simplifying feature preparation, Frame has made ML applications at LinkedIn easier to build, modify, and understand.
MLflow: Infrastructure for a Complete Machine Learning Life Cycle
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.
In this talk, we will present MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
High-speed Database Throughput Using Apache Arrow Flight SQL
Flight SQL is a revolutionary new open database protocol designed for modern architectures. Key features in Flight SQL include a columnar-oriented design and native support for parallel processing of data partitions. This talk will go over how these new features can push SQL query throughput beyond existing standards such as ODBC.
Building End-to-End Delta Pipelines on GCP
Delta has been powering many production pipelines at scale in the Data and AI space since it has been introduced for the past few years.
Built on open standards, Delta provides data reliability, enhances storage and query performance to support big data use cases (both batch and streaming), fast interactive queries for BI and enabling machine learning. Delta has matured over the past couple of years in both AWS and AZURE and has become the de-facto standard for organizations building their Data and AI pipelines.
In today’s talk, we will explore building end-to-end pipelines on the Google Cloud Platform (GCP). Through presentation, code examples and notebooks, we will build the Delta Pipeline from ingest to consumption using our Delta Bronze-Silver-Gold architecture pattern and show examples of Consuming the delta files using the Big Query Connector.
Introduction to RAG (Retrieval Augmented Generation) and its application
Embark on a comprehensive exploration of Retrieval Augmented Generation (RAG) in this illuminating session. Delve into the architecture seamlessly merging retrieval and generation models and uncover its versatile applications. From refining search processes to enhancing content generation, RAG is reshaping the landscape of natural language processing. Join us for a brief yet comprehensive Introduction to RAG and its transformative potential, along with insights into its applications.
Model serving made easy using Kedro pipelines - Mariusz Strzelecki, GetInData
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Presentation from the performance given by Mariusz during the Data Science Summit ML Edition.
Author: Mariusz Strzelecki
Linkedin: https://www.linkedin.com/in/mariusz-strzelecki/
___
Company:
Getindata is a company founded in 2014 by ex-Spotify data engineers. From day one our focus has been on Big Data projects. We bring together a group of best and most experienced experts in Poland, working with cloud and open-source Big Data technologies to help companies build scalable data architectures and implement advanced analytics over large data sets.
Our experts have vast production experience in implementing Big Data projects for Polish as well as foreign companies including i.a. Spotify, Play, Truecaller, Kcell, Acast, Allegro, ING, Agora, Synerise, StepStone, iZettle and many others from the pharmaceutical, media, finance and FMCG industries.
https://getindata.com
Deploying ML models in the enterprise
The talk was given at OReilly Strata Data Conference September 2018 in NYC
All the conferences and thought leaders have been painting a vision of the businesses of the future being powered by data, but if we’re honest with ourselves, the vast majority of our massive data science investments are being deployed to PowerPoint or maybe a business dashboard. Productionizing your machine learning (ML) portfolio is the next big step on the path to ROI from AI.
You probably started out years ago on a “big data” initiative: You collected and cleaned your data and built data warehouses, and when those filled up you upgraded to data lakes. You hired data engineers and data scientists, and around the organization, everyone brushed up their SQL querying skills and got some licenses to Tableau and PowerBI.
Then you saw what Google, Uber, Facebook, and Amazon were doing with machine learning to automate business processes and customer interactions. To not get broadsided, you hired more data scientists and machine learning engineers. They were put on your teams and started using your big data investments to train models. But what you probably found is that your tech stack and DevOps processes don’t fit ML models. Unlike most of your systems, ML models require short spikes of massive compute; they are often written in different languages than your core code; they need different hardware to perform well; one model probably has applications across many teams; and the people making the models often don’t have the engineering experience to write production code but need to iterate faster than traditional engineers. Expecting your engineering and DevOps teams to deploy ML models well is like showing up to Seaworld with a giraffe since they are already handling large mammals.
There is a path forward. Almost five years ago Algorithmia launched a marketplace for models, functions, and algorithms. Today 65,000 developers are on the platform deploying 4,500 models—the result has been a layer of tools and best practices to make deploying ML models frictionless, scalable, and low maintenance. The company refers to it as the “AI layer.”
Drawing on this experience, Diego Oppenheimer covers the strategic and technical hurdles each company must overcome and the best practices developed while deploying over 4,000 ML models for 70,000 engineers.
Topics include:
Best practices for your organization
Continuous model deployment
Varying languages (Your code base probably isn’t in Python or R, but your ML models probably are.)
Managing your portfolio of ML models
Standardize versioning
Enabling models across your organization
Analytics on how and where models are being used
Maintaining auditability
Getting Started with Databricks SQL Analytics
It has long been said that business intelligence needs a relational warehouse, but that view is changing. With the Lakehouse architecture being shouted from the rooftops, Databricks have released SQL Analytics, an alternative workspace for SQL-savvy users to interact with an analytics-tuned cluster. But how does it work? Where do you start? What does a typical Data Analyst’s user journey look like with the tool?
This session will introduce the new workspace and walk through the various key features – how you set up a SQL Endpoint, the query workspace, creating rich dashboards and connecting up BI tools such as Microsoft Power BI.
If you’re truly trying to create a Lakehouse experience that satisfies your SQL-loving Data Analysts, this is a tool you’ll need to be familiar with and include in your design patterns, and this session will set you on the right path.
MLOps Virtual Event: Automating ML at Scale
MLOps Virtual Event: Automating ML at Scale by Matei Zaharia, Chief Technologist, Databrikcs
Apply MLOps at Scale
This session is continuation of “Automated Production Ready ML at Scale” in last Spark AI Summit at Europe. In this session you will learn about how H&M evolves reference architecture covering entire MLOps stack addressing a few common challenges in AI and Machine learning product, like development efficiency, end to end traceability, speed to production, etc.
What's hot (20)
Productionzing ML Model Using MLflow Model Serving
Productionzing ML Model Using MLflow Model Serving
Bighead: Airbnb’s End-to-End Machine Learning Platform with Krishna Puttaswa...
Bighead: Airbnb’s End-to-End Machine Learning Platform with Krishna Puttaswa...
Building a Feature Store around Dataframes and Apache Spark
Building a Feature Store around Dataframes and Apache Spark
Auto-Train a Time-Series Forecast Model With AML + ADB
Auto-Train a Time-Series Forecast Model With AML + ADB
Frame - Feature Management for Productive Machine Learning
Frame - Feature Management for Productive Machine Learning
MLflow: Infrastructure for a Complete Machine Learning Life Cycle
MLflow: Infrastructure for a Complete Machine Learning Life Cycle
High-speed Database Throughput Using Apache Arrow Flight SQL
High-speed Database Throughput Using Apache Arrow Flight SQL
Introduction to RAG (Retrieval Augmented Generation) and its application
Introduction to RAG (Retrieval Augmented Generation) and its application
Model serving made easy using Kedro pipelines - Mariusz Strzelecki, GetInData
Model serving made easy using Kedro pipelines - Mariusz Strzelecki, GetInData
Similar to A Collaborative Data Science Development Workflow
[DSC Europe 23] Petar Zecevic - ML in Production on Databricks
Deploying ML models in production, with or without CI/CD, is significantly more complicated than deploying traditional applications. That is mainly because ML models do not just consist of the code used for their training, but they also depend on the data they are trained on and on the supporting code. Monitoring ML models also adds additional complexity beyond what is usually done for traditional applications. This talk will cover these problems and best practices for solving them, with special focus on how it's done on the Databricks platform.
A practical guidance of the enterprise machine learning
This session provides an analysis of the machine learning market in the enterprise. The analysis includes vendors, platforms and best practices that should be considered by companies implementing data science solutions at an enterprise scale
With Automated ML, is Everyone an ML Engineer?
Google AutoML, AWS SageMaker and other ML tools automate some but not all steps in machine learning workflows. Learn about problem formulation, data engineering, monitoring, and fairness assessment.
Ai & Data Analytics 2018 - Azure Databricks for data scientist
About how a Data Scientists have to use Azure Databricks from a developer enginereen perspective.
Predicting Flights with Azure Databricks
Databricks is a popular tool used with large amounts of data, applying to many roles - including data analysts, data engineers, data scientists, and machine learning engineers. It can be found on many cloud platforms - including Azure, AWS, and GCP. In this talk, we will look at a flight-themed end-to-end solution using Azure Databricks, Azure Data Factory, Azure Storage, and Power BI. By the end of this session, you will have a better understanding of Databricks' capabilities and how it integrates with other Azure offerings.
IncQuery Server for Teamwork Cloud - Talk at IW2019
Slide deck shown at the INCOSE International Workshop 2019.
NextGenML
Revolutionary container based hybrid cloud solution for MLPlatform
Ness' data science platform, NextGenML, puts the entire machine learning process: modelling, execution and deployment in the hands of data science teams.
The entire paradigm approaches collaboration around AI/ML, being implemented with full respect for best practices and commitment to innovation.
Kubernetes (onPrem) + Docker, Azure Kubernetes Cluster (AKS), Nexus, Azure Container Registry(ACR), GlusterFS
Workflow
Argo->Kubeflow
DevOps
Helm, kSonnet, Kustomize,Azure DevOps
Code Management & CI/CD
Git, TeamCity, SonarQube, Jenkins
Security
MS Active Directory, Azure VPN, Dex (K8s) integrated with GitLab
Machine Learning
TensorFlow (model training, boarding, serving), Keras, Seldon
Storage (Azure)
Storage Gen1 & Gen2, Data Lake, File Storage
ETL (Azure)
Databricks, Spark on K8, Data Factory (ADF), HDInsight (Kafka and Spark), Service Bus (ASB)
Lambda functions & VMs, Cache for Redis
Monitoring and Logging
Graphana, Prometeus, GrayLog
Sa introduction to big data pipelining with cassandra & spark west mins...
Slides from my presentation at BigDataWeek London and Cassandra London Meetup 27th November 2015
Tutorial Expert How-To - Command Line Interface (CLI)
Promote a shared understanding of meaning and context for data structures across business users and technical users, through the synchronization and publication of these data structures to business-facing data catalogs.
Global AI Bootcamp Madrid - Azure Databricks
In this session we will delve into the world of Azure Databricks and analyze why it is becoming a tool for data Scientist and/or fundamental data Engineer in conjunction with Azure services
Big Data Adavnced Analytics on Microsoft Azure
This presentation provides a survey of the advanced analytics strengths of Microsoft Azure from an enterprise perspective (with these organizations being the bulk of big data users) based on the Team Data Science Process. The talk also covers the range of analytics and advanced analytics solutions available for developers using data science and artificial intelligence from Microsoft Azure.
Delivering Insights from 20M+ Smart Homes with 500M+ Devices
We started out processing big data using AWS S3, EMR clusters, and Athena to serve Analytics data extracts to Tableau BI.
However as our data and teams sizes increased, Avro schemas from source data evolved, and we attempted to serve analytics data through Web apps, we hit a number of limitations in the AWS EMR, Glue/Athena approach.
This is a story of how we scaled out our data processing and boosted team productivity to meet our current demand for insights from 20M+ Smart Homes and 500M+ devices across the globe, from numerous internal business teams and our 150+ CSP partners.
We will describe lessons learnt and best practices established as we enabled our teams with DataBricks autoscaling Job clusters and Notebooks and migrated our Avro/Parquet data to use MetaStore, SQL Endpoints and SQLA Console, while charting the path to the Delta lake…
The Internet of Simulations and the agile development of Cyber-physical systems
Presentation made in the 2017 IEEE System of Systems conference. Co-authored by Stephen Clement, David McKee, Richard Romano and Jie Xu (University of Leeds), Jose-Maria Lopez (Simware Solutions) and David Battersby (Jaguar Land Rover)
How a Data Mesh is Driving our Platform | Trey Hicks, Gloo
At Gloo.us, we face a challenge in providing platform data to heterogeneous applications in a way that eliminates access contention, avoids high latency ETLs, and ensures consistency for many teams. We're solving this problem by adopting Data Mesh principles and leveraging Kafka, Kafka Connect, and Kafka streams to build an event driven architecture to connect applications to the data they need. A domain driven design keeps the boundaries between specialized process domains and singularly focused data domains clear, distinct, and disciplined. Applying the principles of a Data Mesh, process domains assume the responsibility of transforming, enriching, or aggregating data rather than relying on these changes at the source of truth -- the data domains. Architecturally, we've broken centralized big data lakes into smaller data stores that can be consumed into storage managed by process domains.
This session covers how we’re applying Kafka tools to enable our data mesh architecture. This includes how we interpret and apply the data mesh paradigm, the role of Kafka as the backbone for a mesh of connectivity, the role of Kafka Connect to generate and consume data events, and the use of KSQL to perform minor transformations for consumers.
IBM InterConnect 2015 - IIB Effective Application Development
IBM Integration Bus - Effective Application Development; IBM InterConnect Conference, Feb 2015
Machine Learning and AI
The breath and depth of Azure products that fall under the AI and ML umbrella can be difficult to follow. In this presentation I’ll first define exactly what AI, ML, and deep learning is, and then go over the various Microsoft AI and ML products and their use cases.
Etosha - Data Asset Manager : Status and road map
Etosha is an enterprise focused collaborative graph database with facts about data sets, analysis procedures, and research methods. People from multiple organizations can be connected while every owner retains full control about its own data.
Accelerating Machine Learning on Databricks Runtime
"We all know the unprecedented potential impact for Machine Learning. But how do you take advantage of the myriad of data and ML tools now available? How do you streamline processes, speed up discovery, share knowledge, and scale up implementations for real-life scenarios?
In this talk, we'll cover some of the latest innovations brought into the Databricks Unified Analytics Platform for Machine Learning. In particular we will show you how to:
- Get started quickly using the Databricks Runtime for Machine Learning, that provides pre-configured Databricks clusters including the most popular ML frameworks and libraries, Conda support, performance optimizations, and more.
- Get started with most popular Deep Learning frameworks within a few minutes and go deep with state of the art model DL diagnostics tools.
- Scale up Deep Learning training workloads from a single machine to large clusters for the most demanding applications using the new HorovodRunner with ease.
- How all of these ML frameworks get exposed to large and distributed data using Databricks Runtime for Machine Learning."
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[DSC Europe 23] Petar Zecevic - ML in Production on Databricks
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A practical guidance of the enterprise machine learning
A practical guidance of the enterprise machine learning
Ai & Data Analytics 2018 - Azure Databricks for data scientist
Ai & Data Analytics 2018 - Azure Databricks for data scientist
IncQuery Server for Teamwork Cloud - Talk at IW2019
IncQuery Server for Teamwork Cloud - Talk at IW2019
Sa introduction to big data pipelining with cassandra & spark west mins...
Sa introduction to big data pipelining with cassandra & spark west mins...
Tutorial Expert How-To - Command Line Interface (CLI)
Tutorial Expert How-To - Command Line Interface (CLI)
Delivering Insights from 20M+ Smart Homes with 500M+ Devices
Delivering Insights from 20M+ Smart Homes with 500M+ Devices
The Internet of Simulations and the agile development of Cyber-physical systems
The Internet of Simulations and the agile development of Cyber-physical systems
How a Data Mesh is Driving our Platform | Trey Hicks, Gloo
How a Data Mesh is Driving our Platform | Trey Hicks, Gloo
IBM InterConnect 2015 - IIB Effective Application Development
IBM InterConnect 2015 - IIB Effective Application Development
Accelerating Machine Learning on Databricks Runtime
Accelerating Machine Learning on Databricks Runtime
More from Databricks
Data Lakehouse Symposium | Day 1 | Part 1
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Data Lakehouse Symposium | Day 1 | Part 2
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Data Lakehouse Symposium | Day 2
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Data Lakehouse Symposium | Day 4
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
In this session, learn how to quickly supplement your on-premises Hadoop environment with a simple, open, and collaborative cloud architecture that enables you to generate greater value with scaled application of analytics and AI on all your data. You will also learn five critical steps for a successful migration to the Databricks Lakehouse Platform along with the resources available to help you begin to re-skill your data teams.
Democratizing Data Quality Through a Centralized Platform
Bad data leads to bad decisions and broken customer experiences. Organizations depend on complete and accurate data to power their business, maintain efficiency, and uphold customer trust. With thousands of datasets and pipelines running, how do we ensure that all data meets quality standards, and that expectations are clear between producers and consumers? Investing in shared, flexible components and practices for monitoring data health is crucial for a complex data organization to rapidly and effectively scale.
At Zillow, we built a centralized platform to meet our data quality needs across stakeholders. The platform is accessible to engineers, scientists, and analysts, and seamlessly integrates with existing data pipelines and data discovery tools. In this presentation, we will provide an overview of our platform’s capabilities, including:
Giving producers and consumers the ability to define and view data quality expectations using a self-service onboarding portal
Performing data quality validations using libraries built to work with spark
Dynamically generating pipelines that can be abstracted away from users
Flagging data that doesn’t meet quality standards at the earliest stage and giving producers the opportunity to resolve issues before use by downstream consumers
Exposing data quality metrics alongside each dataset to provide producers and consumers with a comprehensive picture of health over time
Learn to Use Databricks for Data Science
Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
Why APM Is Not the Same As ML Monitoring
Application performance monitoring (APM) has become the cornerstone of software engineering allowing engineering teams to quickly identify and remedy production issues. However, as the world moves to intelligent software applications that are built using machine learning, traditional APM quickly becomes insufficient to identify and remedy production issues encountered in these modern software applications.
As a lead software engineer at NewRelic, my team built high-performance monitoring systems including Insights, Mobile, and SixthSense. As I transitioned to building ML Monitoring software, I found the architectural principles and design choices underlying APM to not be a good fit for this brand new world. In fact, blindly following APM designs led us down paths that would have been better left unexplored.
In this talk, I draw upon my (and my team’s) experience building an ML Monitoring system from the ground up and deploying it on customer workloads running large-scale ML training with Spark as well as real-time inference systems. I will highlight how the key principles and architectural choices of APM don’t apply to ML monitoring. You’ll learn why, understand what ML Monitoring can successfully borrow from APM, and hear what is required to build a scalable, robust ML Monitoring architecture.
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
Autonomy and ownership are core to working at Stitch Fix, particularly on the Algorithms team. We enable data scientists to deploy and operate their models independently, with minimal need for handoffs or gatekeeping. By writing a simple function and calling out to an intuitive API, data scientists can harness a suite of platform-provided tooling meant to make ML operations easy. In this talk, we will dive into the abstractions the Data Platform team has built to enable this. We will go over the interface data scientists use to specify a model and what that hooks into, including online deployment, batch execution on Spark, and metrics tracking and visualization.
Stage Level Scheduling Improving Big Data and AI Integration
In this talk, I will dive into the stage level scheduling feature added to Apache Spark 3.1. Stage level scheduling extends upon Project Hydrogen by improving big data ETL and AI integration and also enables multiple other use cases. It is beneficial any time the user wants to change container resources between stages in a single Apache Spark application, whether those resources are CPU, Memory or GPUs. One of the most popular use cases is enabling end-to-end scalable Deep Learning and AI to efficiently use GPU resources. In this type of use case, users read from a distributed file system, do data manipulation and filtering to get the data into a format that the Deep Learning algorithm needs for training or inference and then sends the data into a Deep Learning algorithm. Using stage level scheduling combined with accelerator aware scheduling enables users to seamlessly go from ETL to Deep Learning running on the GPU by adjusting the container requirements for different stages in Spark within the same application. This makes writing these applications easier and can help with hardware utilization and costs.
There are other ETL use cases where users want to change CPU and memory resources between stages, for instance there is data skew or perhaps the data size is much larger in certain stages of the application. In this talk, I will go over the feature details, cluster requirements, the API and use cases. I will demo how the stage level scheduling API can be used by Horovod to seamlessly go from data preparation to training using the Tensorflow Keras API using GPUs.
The talk will also touch on other new Apache Spark 3.1 functionality, such as pluggable caching, which can be used to enable faster dataframe access when operating from GPUs.
Simplify Data Conversion from Spark to TensorFlow and PyTorch
In this talk, I would like to introduce an open-source tool built by our team that simplifies the data conversion from Apache Spark to deep learning frameworks.
Imagine you have a large dataset, say 20 GBs, and you want to use it to train a TensorFlow model. Before feeding the data to the model, you need to clean and preprocess your data using Spark. Now you have your dataset in a Spark DataFrame. When it comes to the training part, you may have the problem: How can I convert my Spark DataFrame to some format recognized by my TensorFlow model?
The existing data conversion process can be tedious. For example, to convert an Apache Spark DataFrame to a TensorFlow Dataset file format, you need to either save the Apache Spark DataFrame on a distributed filesystem in parquet format and load the converted data with third-party tools such as Petastorm, or save it directly in TFRecord files with spark-tensorflow-connector and load it back using TFRecordDataset. Both approaches take more than 20 lines of code to manage the intermediate data files, rely on different parsing syntax, and require extra attention for handling vector columns in the Spark DataFrames. In short, all these engineering frictions greatly reduced the data scientists’ productivity.
The Databricks Machine Learning team contributed a new Spark Dataset Converter API to Petastorm to simplify these tedious data conversion process steps. With the new API, it takes a few lines of code to convert a Spark DataFrame to a TensorFlow Dataset or a PyTorch DataLoader with default parameters.
In the talk, I will use an example to show how to use the Spark Dataset Converter to train a Tensorflow model and how simple it is to go from single-node training to distributed training on Databricks.
Scaling your Data Pipelines with Apache Spark on Kubernetes
There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal.
In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Pipelines have become ubiquitous, as the need for stringing multiple functions to compose applications has gained adoption and popularity. Common pipeline abstractions such as “fit” and “transform” are even shared across divergent platforms such as Python Scikit-Learn and Apache Spark.
Scaling pipelines at the level of simple functions is desirable for many AI applications, however is not directly supported by Ray’s parallelism primitives. In this talk, Raghu will describe a pipeline abstraction that takes advantage of Ray’s compute model to efficiently scale arbitrarily complex pipeline workflows. He will demonstrate how this abstraction cleanly unifies pipeline workflows across multiple platforms such as Scikit-Learn and Spark, and achieves nearly optimal scale-out parallelism on pipelined computations.
Attendees will learn how pipelined workflows can be mapped to Ray’s compute model and how they can both unify and accelerate their pipelines with Ray.
Sawtooth Windows for Feature Aggregations
In this talk about zipline, we will introduce a new type of windowing construct called a sawtooth window. We will describe various properties about sawtooth windows that we utilize to achieve online-offline consistency, while still maintaining high-throughput, low-read latency and tunable write latency for serving machine learning features.We will also talk about a simple deployment strategy for correcting feature drift – due operations that are not “abelian groups”, that operate over change data.
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark.
Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue
· Why?
o Custom queries on top a table; We load the data once and query N times
· Why not Structured Streaming
· Working Solution using Redis
Niche 2 : Distributed Counters
· Problems with Spark Accumulators
· Utilize Redis Hashes as distributed counters
· Precautions for retries and speculative execution
· Pipelining to improve performance
Re-imagine Data Monitoring with whylogs and Spark
In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data.
In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.
Raven: End-to-end Optimization of ML Prediction Queries
Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components.
We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure.
This allows us to introduce optimization rules that
(i) reduce unnecessary computations by passing information between the data processing and ML operators
(ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and
(iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator.
We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.
Processing Large Datasets for ADAS Applications using Apache Spark
Semantic segmentation is the classification of every pixel in an image/video. The segmentation partitions a digital image into multiple objects to simplify/change the representation of the image into something that is more meaningful and easier to analyze [1][2]. The technique has a wide variety of applications ranging from perception in autonomous driving scenarios to cancer cell segmentation for medical diagnosis.
Exponential growth in the datasets that require such segmentation is driven by improvements in the accuracy and quality of the sensors generating the data extending to 3D point cloud data. This growth is further compounded by exponential advances in cloud technologies enabling the storage and compute available for such applications. The need for semantically segmented datasets is a key requirement to improve the accuracy of inference engines that are built upon them.
Streamlining the accuracy and efficiency of these systems directly affects the value of the business outcome for organizations that are developing such functionalities as a part of their AI strategy.
This presentation details workflows for labeling, preprocessing, modeling, and evaluating performance/accuracy. Scientists and engineers leverage domain-specific features/tools that support the entire workflow from labeling the ground truth, handling data from a wide variety of sources/formats, developing models and finally deploying these models. Users can scale their deployments optimally on GPU-based cloud infrastructure to build accelerated training and inference pipelines while working with big datasets. These environments are optimized for engineers to develop such functionality with ease and then scale against large datasets with Spark-based clusters on the cloud.
Massive Data Processing in Adobe Using Delta Lake
At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences.
What are we storing?
Multi Source – Multi Channel Problem
Data Representation and Nested Schema Evolution
Performance Trade Offs with Various formats
Go over anti-patterns used
(String FTW)
Data Manipulation using UDFs
Writer Worries and How to Wipe them Away
Staging Tables FTW
Datalake Replication Lag Tracking
Performance Time!
More from Databricks (20)
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
Democratizing Data Quality Through a Centralized Platform
Democratizing Data Quality Through a Centralized Platform
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
Stage Level Scheduling Improving Big Data and AI Integration
Stage Level Scheduling Improving Big Data and AI Integration
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Scaling your Data Pipelines with Apache Spark on Kubernetes
Scaling your Data Pipelines with Apache Spark on Kubernetes
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Raven: End-to-end Optimization of ML Prediction Queries
Raven: End-to-end Optimization of ML Prediction Queries
Processing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache Spark
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6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
教育部文凭学历认证认证的用途：
如果您计划在国内发展那么办理国内教育部认证是必不可少的。事业性用人单位如银行国企公务员在您应聘时都会需要您提供这个认证。其他私营 #外企企业无需提供！办理教育部认证所需资料众多且烦琐所有材料您都必须提供原件我们凭借丰富的经验帮您快速整合材料让您少走弯路。
实体公司专业为您服务如有需要请联系我: 微信95270640声和哐咣的关门声待山娃醒来时父亲早已上班去了床头总搁着山娃最爱吃的馒头和肉包还有白花花的豆浆父亲中午留在工地吃饭和午休山娃的中饭是对面快餐店送来的不用山娃付钱父亲早跟老板谈妥了钱到时一起结父亲给山娃配了台手机二手货诺基亚的父亲说有什么事只管给他挂电话能拥有自己的手机山娃很高兴除了玩游戏发短信除了挂电话给爷爷奶奶和母亲山娃还给班主任邱老师连挂了二个电话并给同学阿强和阿昌家挂山娃兴奋地向他们诉说城市一
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
一比一原版(CU毕业证)卡尔顿大学毕业证成绩单
CU毕业证【微信95270640】（卡尔顿大学毕业证成绩单本科学历）Q微信95270640(补办CU学位文凭证书)卡尔顿大学留信网学历认证怎么办理卡尔顿大学毕业证成绩单精仿本科学位证书硕士文凭证书认证Seneca College diplomaoffer,Transcript办理硕士学位证书造假卡尔顿大学假文凭学位证书制作CU本科毕业证书硕士学位证书精仿卡尔顿大学学历认证成绩单修改制作，办理真实认证、留信认证、使馆公证、购买成绩单，购买假文凭，购买假学位证，制造假国外大学文凭、毕业公证、毕业证明书、录取通知书、Offer、在读证明、雅思托福成绩单、假文凭、假毕业证、请假条、国际驾照、网上存档可查！
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我们从事工作十余年的有着丰富经验的业务顾问，熟悉海外各国大学的学制及教育体系，并且以挂科生解决毕业材料不全问题为基础，为客户量身定制1对1方案，未能毕业的回国留学生成功搭建回国顺利发展所需的桥梁。我们一直努力以高品质的教育为起点，以诚信、专业、高效、创新作为一切的行动宗旨，始终把“诚信为主、质量为本、客户第一”作为我们全部工作的出发点和归宿点。同时为海内外留学生提供大学毕业证购买、补办成绩单及各类分数修改等服务；归国认证方面，提供《留信网入库》申请、《国外学历学位认证》申请以及真实学籍办理等服务，帮助众多莘莘学子实现了一个又一个梦想。
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你赢我赢，共创双赢
你做代理，可以帮助卡尔顿大学同学朋友
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你做代理，可以改变自己，改变他人，给他人和自己一个机会他交友与城里人交友但他俩就好像是两个世界里的人根本拢不到一块儿不知不觉山娃倒跟周围出租屋里的几个小伙伴成了好朋友因为他们也是从乡下进城过暑假的小学生快乐的日子总是过得飞快山娃尚未完全认清那几位小朋友时他们却一个接一个地回家了山娃这时才恍然发现二个月的暑假已转到了尽头他的城市生活也将划上一个不很圆满的句号了值得庆幸的是山娃早记下了他们的学校和联系方式说也奇怪在山娃离城的头一天父亲居然请假陪山娃耍了活
做(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书学费发票原版一模一样
原版定制【Q微信:741003700】《(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书》【Q微信:741003700】成绩单 、雅思、外壳、留信学历认证永久存档查询，采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【Q微信741003700】文凭即可
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留信网认证的作用:
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我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
一比一原版(TWU毕业证)西三一大学毕业证成绩单
TWU毕业证【微信95270640】西三一大学没毕业>办理西三一大学毕业证成绩单【微信TWU】TWU毕业证成绩单TWU学历证书TWU文凭《TWU毕业套号文凭网认证西三一大学毕业证成绩单》《哪里买西三一大学毕业证文凭TWU成绩学校快递邮寄信封》《开版西三一大学文凭》TWU留信认证本科硕士学历认证
[留学文凭学历认证(留信认证使馆认证)西三一大学毕业证成绩单毕业证证书大学Offer请假条成绩单语言证书国际回国人员证明高仿教育部认证申请学校等一切高仿或者真实可查认证服务。
多年留学服务公司,拥有海外样板无数能完美1:1还原海外各国大学degreeDiplomaTranscripts等毕业材料。海外大学毕业材料都有哪些工艺呢？工艺难度主要由：烫金.钢印.底纹.水印.防伪光标.热敏防伪等等组成。而且我们每天都在更新海外文凭的样板以求所有同学都能享受到完美的品质服务。
国外毕业证学位证成绩单办理方法：
1客户提供办理西三一大学西三一大学本科学位证成绩单信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
— — — — 我们是挂科和未毕业同学们的福音我们是实体公司精益求精的工艺！ — — — -
一真实留信认证的作用(私企外企荣誉的见证):
1：该专业认证可证明留学生真实留学身份同时对留学生所学专业等级给予评定。
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4：个人职称评审加20分个人信誉贷款加10分。
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一比一原版(UVic毕业证)维多利亚大学毕业证成绩单
UVic毕业证【微信95270640】（维多利亚大学毕业证成绩单本科学历）Q微信95270640(补办UVic学位文凭证书)维多利亚大学留信网学历认证怎么办理维多利亚大学毕业证成绩单精仿本科学位证书硕士文凭证书认证Seneca College diplomaoffer,Transcript办理硕士学位证书造假维多利亚大学假文凭学位证书制作UVic本科毕业证书硕士学位证书精仿维多利亚大学学历认证成绩单修改制作，办理真实认证、留信认证、使馆公证、购买成绩单，购买假文凭，购买假学位证，制造假国外大学文凭、毕业公证、毕业证明书、录取通知书、Offer、在读证明、雅思托福成绩单、假文凭、假毕业证、请假条、国际驾照、网上存档可查！
【实体公司】办维多利亚大学维多利亚大学毕业证文凭证书学历认证学位证文凭认证办留信网认证办留服认证办教育部认证（网上可查实体公司专业可靠）
— — — 留学归国服务中心 — — -
【主营项目】
一.维多利亚大学毕业证成绩单使馆认证教育部认证成绩单等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
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四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
国外毕业证学位证成绩单办理流程：
1客户提供维多利亚大学维多利亚大学毕业证文凭证书办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
专业服务请勿犹豫联系我！本公司是留学创业和海归创业者们的桥梁。一次办理终生受用一步到位高效服务。详情请在线咨询办理,欢迎有诚意办理的客户咨询!洽谈。
招聘代理：本公司诚聘英国加拿大澳洲新西兰美国法国德国新加坡各地代理人员如果你有业余时间有兴趣就请联系我们咨询顾问：+微信:95270640刀劈开抑或用拳头砸开每人抱起一大块就啃啃得满嘴满脸猴屁股般的红艳大家一个劲地指着对方吃吃地笑瓜裂得古怪奇形怪状却丝毫不影响瓜味甜丝丝的满嘴生津遍地都是瓜横七竖八的活像掷满了一地的大石块摘走二三只爷爷是断然发现不了的即便发现爷爷也不恼反而教山娃辨认孰熟孰嫩孰甜孰淡名义上是护瓜往往在瓜棚里坐上一刻饱吃一顿后山娃就领着阿黑漫山遍野地跑阿黑是一条黑色的大猎狗挺机灵的是山娃多年的忠实伙伴平时山娃上学阿黑也静
Adjusting primitives for graph : SHORT REPORT / NOTES
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Empowering Data Analytics Ecosystem.pptx
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单
UPenn毕业证【微信95270640】办理宾夕法尼亚大学毕业证原版一模一样、UPenn毕业证制作【Q微信95270640】《宾夕法尼亚大学毕业证购买流程》《UPenn成绩单制作》宾夕法尼亚大学毕业证书UPenn毕业证文凭宾夕法尼亚大学
本科毕业证书,学历学位认证如何办理【留学国外学位学历认证、毕业证、成绩单、大学Offer、雅思托福代考、语言证书、学生卡、高仿教育部认证等一切高仿或者真实可查认证服务】代办国外（海外）英国、加拿大、美国、新西兰、澳大利亚、新西兰等国外各大学毕业证、文凭学历证书、成绩单、学历学位认证真实可查。
办国外宾夕法尼亚大学宾夕法尼亚大学硕士学位证成绩单教育部学历学位认证留信认证大使馆认证留学回国人员证明修改成绩单信封申请学校offer录取通知书在读证明offer letter。
快速办理高仿国外毕业证成绩单：
1宾夕法尼亚大学毕业证+成绩单+留学回国人员证明+教育部学历认证（全套留学回国必备证明材料给父母及亲朋好友一份完美交代）;
2雅思成绩单托福成绩单OFFER在读证明等留学相关材料（申请学校转学甚至是申请工签都可以用到）。
3.毕业证 #成绩单等全套材料从防伪到印刷从水印到钢印烫金高精仿度跟学校原版100%相同。
专业服务请勿犹豫联系我！联系人微信号：95270640诚招代理：本公司诚聘当地代理人员如果你有业余时间有兴趣就请联系我们。
国外宾夕法尼亚大学宾夕法尼亚大学硕士学位证成绩单办理过程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。我们在哪里父母对我们的爱和思念为我们的生命增加了光彩给予我们自由追求的力量生活的力量我们也不忘感恩正因为这股感恩的线牵着我们使我们在一年的结束时刻义无反顾的踏上了回家的旅途人们常说父母恩最难回报愿我能以当年爸爸妈妈对待小时候的我们那样耐心温柔地对待我将渐渐老去的父母体谅他们以反哺之心奉敬父母以感恩之心孝顺父母哪怕只为父母换洗衣服为父母喂饭送汤按摩酸痛的腰背握着父母的手扶着他们一步一步地慢慢散步.娃
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
UofM毕业证【微信95270640】办文凭{明尼苏达大学毕业证}Q微Q微信95270640UofM毕业证书成绩单/学历认证UofM Diploma未毕业、挂科怎么办？+QQ微信：Q微信95270640-大学Offer（申请大学）、成绩单（申请考研）、语言证书、在读证明、使馆公证、办真实留信网认证、真实大使馆认证、学历认证
办国外明尼苏达大学明尼苏达大学毕业证假文凭教育部学历学位认证留信认证大使馆认证留学回国人员证明修改成绩单信封申请学校offer录取通知书在读证明offer letter。
快速办理高仿国外毕业证成绩单：
1明尼苏达大学毕业证+成绩单+留学回国人员证明+教育部学历认证（全套留学回国必备证明材料给父母及亲朋好友一份完美交代）;
2雅思成绩单托福成绩单OFFER在读证明等留学相关材料（申请学校转学甚至是申请工签都可以用到）。
3.毕业证 #成绩单等全套材料从防伪到印刷从水印到钢印烫金高精仿度跟学校原版100%相同。
专业服务请勿犹豫联系我！联系人微信号：95270640诚招代理：本公司诚聘当地代理人员如果你有业余时间有兴趣就请联系我们。
国外明尼苏达大学明尼苏达大学毕业证假文凭办理过程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。有一次山娃坐在门口写作业写着写着竟伏在桌上睡着了迷迷糊糊中山娃似乎听到了父亲的脚步声当他晃晃悠悠站起来时才诧然发现一位衣衫破旧的妇女挎着一只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃母
一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单
UMich毕业证【微信95270640】办理UMich毕业证【Q微信95270640】密歇根大学|安娜堡分校毕业证书原版↑制作密歇根大学|安娜堡分校学历认证文凭办理密歇根大学|安娜堡分校留信网认证,留学回国办理毕业证成绩单文凭学历认证【Q微信95270640】专业为海外学子办理毕业证成绩单、文凭制作，学历仿制，回国人员证明、做文凭，研究生、本科、硕士学历认证、留信认证、结业证、学位证书样本、美国教育部认证百分百真实存档可查】
【本科硕士】密歇根大学|安娜堡分校密歇根大学|安娜堡分校硕士毕业证成绩单（GPA修改）；学历认证（教育部认证）；大学Offer录取通知书留信认证使馆认证；雅思语言证书等高仿类证书。
办理流程：
1客户提供办理密歇根大学|安娜堡分校密歇根大学|安娜堡分校硕士毕业证成绩单信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
真实网上可查的证明材料
1教育部学历学位认证留服官网真实存档可查永久存档。
2留学回国人员证明（使馆认证）使馆网站真实存档可查。
我们对海外大学及学院的毕业证成绩单所使用的材料尺寸大小防伪结构（包括：密歇根大学|安娜堡分校密歇根大学|安娜堡分校硕士毕业证成绩单隐形水印阴影底纹钢印LOGO烫金烫银LOGO烫金烫银复合重叠。文字图案浮雕激光镭射紫外荧光温感复印防伪）都有原版本文凭对照。质量得到了广大海外客户群体的认可同时和海外学校留学中介做到与时俱进及时掌握各大院校的（毕业证成绩单资格证结业证录取通知书在读证明等相关材料）的版本更新信息能够在第一时间掌握最新的海外学历文凭的样版尺寸大小纸张材质防伪技术等等并在第一时间收集到原版实物以求达到客户的需求。
本公司还可以按照客户原版印刷制作且能够达到客户理想的要求。有需要办理证件的客户请联系我们在线客服中心微信：95270640 或咨询在线在我脑海中回旋很幸运我有这样的机会能将埋在心底深深的感激之情得以表达从婴儿的“哇哇坠地到哺育我长大您们花去了多少的心血与汗水编织了多少个日日夜夜从上小学到初中乃至大学燃烧着自己默默奉献着光和热全天下的父母都是仁慈的无私的伟大的所以无论何时何地都不要忘记您们是这样的无私将一份沉甸甸的爱和希望传递到我们的眼里和心中而我应做的就是感恩父母回报社会共创和谐父母对我们的恩情深厚而无私孝敬父母是我们做人元
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A Collaborative Data Science Development Workflow
- 1. A Collaborative and Scalable Machine Learning Workflow Nick Hale Senior Researcher
- 2. Agenda § Overview § Components § Architecture § Project Workflow
- 3. Overview • Objectives • A cost effective and scalable process for collaborative Machine Learning (ML) R&D • A framework for comparing experiments and deploying production models • A cloud agnostic ML training and deployment framework • Core Contributions • Git version control method suited for collaborative ML development • A method for scaling data science pipelines • A tightly integrated method for collaborative data science, experiment tracking, and model deployment
- 4. Components
- 5. Components • Environment Consistency – Docker • Docker is an open platform for developing and running applications • Docker uses loosely isolated environments called Containers that include everything needed to develop and run code • Docker is used to create a consistent environment (dependencies, libraries, etc.) for ML among developers, model training runs, and deployments
- 6. Components • ML Pipelines – Kedro • Open-source Python framework for reproducible, maintainable, and modular data science code from QuantumBlack • Creates Directed Acyclic Graphs (DAGs) comprised of functions and datasets called pipelines • Nodes in the pipeline are functions that can be data transformations, model training, etc. • Uses data engineering convention to track data transformations across local and cloud datastores
- 7. Example Pipeline Data Engineering Convention
- 8. Components • ML Experiment Tracking– MLflow • Open-source platform for managing the ML lifecycle, including experiments, reproducibility, deployment, and model registry • ML tests from multiple users and compute environments tracked and compared • Models can be deployed to production environments directly from MLflow and predictions can be served through REST APIs • Model versions and parameters are tracked so that models can always be reproduced
- 9. MLflow Server User Interface
- 10. Components • ML lifecycle Integration – Databricks • Databricks is a platform that enables seamless integration of data science code, data, experiment tracking, and cloud resources • Databricks allows data scientists to easily run local Kedro pipelines on compute clusters via the Databricks Connect library • Databricks Connect is a Spark client library that connects local development environments to Databricks clusters • Databricks allows for easy logging of ML experiments in MLflow
- 11. Components • Big data processing – PySpark • PySpark is a python library that ships distributed data science jobs to a Spark cluster running on Databricks • PySpark and Databricks Connect allows Kedro pipelines developed on a local machine to run on the cloud with no friction • Koalas implements the native Pandas DataFrame on top of Spark which allows scalable computing with a minimal learning curve
- 12. Local Machines / Individual Developer Compute Docker/Git repositories Cloud ML Scaffold Repo (custom ml functions) New ML Project Repo (Kedroized) Training Compute Deployment Compute Databricks base Image databricks-connect Install/clone ML Libraries Pipelines Spark Jobs / Pipelines Kedro Data Engineering Convention Architecture
- 13. Compute Local Machine Dev Env Inference Machine Development Pipelines + experiments Candidate model training Tracking Logging Experiments Register/version models Deploy Final Model Serving Model Project Set-up Git Flow Project Template Dev/Training Container Scaffold Library pip install Cloud Compute Init master/dev with project set-up ML Engineer dev branches dev master Logging/ versioning Model Re-training New function (s) depend encies to scaffold Cloud Compute Evaluate models/code Select final model databricks-connect Kedro Data Engineering Step 1. 2. 3. 4. 5.