Spark and machine learning in microservices architecture
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Modern architecture for machine learning and state-safe data processing. Data Pipelines and machine learning models deployment in production.
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Spark ML Pipeline serving
My talk at Data Science Labs conference in Odessa.
Training a model in Apache Spark while having it automatically available for real-time serving is an essential feature for end-to-end solutions.
There is an option to export the model into PMML and then import it into a separated scoring engine. The idea of interoperability is great but it has multiple challenges, such as code duplication, limited extensibility, inconsistency, extra moving parts. In this talk we discussed an alternative solution that does not introduce custom model formats and new standards, not based on export/import workflow and shares Apache Spark API.
Apache ® Spark™ MLlib 2.x: How to Productionize your Machine Learning Models
Apache Spark has rapidly become a key tool for data scientists to explore, understand and transform massive datasets and to build and train advanced machine learning models. The question then becomes, how do I deploy these model to a production environment? How do I embed what I have learned into customer facing data applications?
In this webinar, we will discuss best practices from Databricks on
how our customers productionize machine learning models
do a deep dive with actual customer case studies,
show live tutorials of a few example architectures and code in Python, Scala, Java and SQL.
Serverless machine learning operations
Any startup has to have a clear go-to-market strategy from the beginning. Similarly, any data science project has to have a go-to-production strategy from its first days, so it could go beyond proof-of-concept. Machine learning and artificial intelligence in production would result in hundreds of training pipelines and machine learning models that are continuously revised by teams of data scientists and seamlessly connected with web applications for tenants and users.
In this demo-based talk we will walk through the best practices for simplifying machine learning operations across the enterprise and providing a serverless abstraction for data scientists and data engineers, so they could train, deploy and monitor machine learning models faster and with better quality.
Deploying and Monitoring Heterogeneous Machine Learning Applications with Cli...
Machine learning is being deployed in a growing number of applications which demand real-time, accurate, and robust predictions under heavy serving loads. However, most machine learning frameworks and systems only address model training and not deployment.
Clipper is an open-source, general-purpose model-serving system that addresses these challenges. Interposing between applications that consume predictions and the machine-learning models that produce predictions, Clipper simplifies the model deployment process by adopting a modular serving architecture and isolating models in their own containers, allowing them to be evaluated using the same runtime environment as that used during training. Clipper’s modular architecture provides simple mechanisms for scaling out models to meet increased throughput demands and performing fine-grained physical resource allocation for each model. Further, by abstracting models behind a uniform serving interface, Clipper allows developers to compose many machine-learning models within a single application to support increasingly common techniques such as ensemble methods, multi-armed bandit algorithms, and prediction cascades.
In this talk I will provide an overview of the Clipper serving system and discuss how to get started using Clipper to serve Apache Spark and TensorFlow models on Kubernetes. I will then discuss some recent work on statistical performance monitoring for machine learning models.
MLOps with a Feature Store: Filling the Gap in ML Infrastructure
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. The first Feature Stores, developed at hyperscale AI companies such as Uber, Airbnb, and Facebook, enabled feature engineering using domain specific languages, providing abstractions tailored to the companies’ feature engineering domains. However, a general purpose Feature Store needs a general purpose feature engineering, feature selection, and feature transformation platform.
In this talk, we describe how we built a general purpose, open-source Feature Store for ML around dataframes and Apache Spark. We will demonstrate how data engineers can transform and engineers features from backend databases and data lakes, while data scientists can use PySpark to select and transform features into train/test data in a file format of choice (.tfrecords, .npy, .petastorm, etc) on a file system of choice (S3, HDFS). Finally, we will show how the Feature Store enables end-to-end ML pipelines to be factored into feature engineering and data science stages that each can run at different cadences.
Bio:
Fabio Buso is the head of engineering at Logical Clocks AB, where he leads the Feature Store development. Fabio holds a master's degree in cloud computing and services with a focus on data intensive applications, awarded by a joint program between KTH Stockholm and TU Berlin.
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The talk I gave at Scale By The Bay.
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Apache Spark has rapidly become a key tool for data scientists to explore, understand and transform massive datasets and to build and train advanced machine learning models. The question then becomes, how do you deploy these ML model to a production environment? How do you embed what you’ve learned into customer facing data applications?
In this talk I will discuss best practices on how data scientists productionize machine learning models, do a deep dive with actual case studies, and show live tutorials of a few example architectures and code in Python, Scala, Java and SQL.
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If you're thinking about machine learning and not sure if it can help improve your business, but want to find out, set up a free 20-minute consultation with us: https://calendly.com/robertwdempsey/free-consultation
Recommended
Spark ML Pipeline serving
My talk at Data Science Labs conference in Odessa.
Training a model in Apache Spark while having it automatically available for real-time serving is an essential feature for end-to-end solutions.
There is an option to export the model into PMML and then import it into a separated scoring engine. The idea of interoperability is great but it has multiple challenges, such as code duplication, limited extensibility, inconsistency, extra moving parts. In this talk we discussed an alternative solution that does not introduce custom model formats and new standards, not based on export/import workflow and shares Apache Spark API.
Apache ® Spark™ MLlib 2.x: How to Productionize your Machine Learning Models
Apache Spark has rapidly become a key tool for data scientists to explore, understand and transform massive datasets and to build and train advanced machine learning models. The question then becomes, how do I deploy these model to a production environment? How do I embed what I have learned into customer facing data applications?
In this webinar, we will discuss best practices from Databricks on
how our customers productionize machine learning models
do a deep dive with actual customer case studies,
show live tutorials of a few example architectures and code in Python, Scala, Java and SQL.
Serverless machine learning operations
Any startup has to have a clear go-to-market strategy from the beginning. Similarly, any data science project has to have a go-to-production strategy from its first days, so it could go beyond proof-of-concept. Machine learning and artificial intelligence in production would result in hundreds of training pipelines and machine learning models that are continuously revised by teams of data scientists and seamlessly connected with web applications for tenants and users.
In this demo-based talk we will walk through the best practices for simplifying machine learning operations across the enterprise and providing a serverless abstraction for data scientists and data engineers, so they could train, deploy and monitor machine learning models faster and with better quality.
Deploying and Monitoring Heterogeneous Machine Learning Applications with Cli...
Machine learning is being deployed in a growing number of applications which demand real-time, accurate, and robust predictions under heavy serving loads. However, most machine learning frameworks and systems only address model training and not deployment.
Clipper is an open-source, general-purpose model-serving system that addresses these challenges. Interposing between applications that consume predictions and the machine-learning models that produce predictions, Clipper simplifies the model deployment process by adopting a modular serving architecture and isolating models in their own containers, allowing them to be evaluated using the same runtime environment as that used during training. Clipper’s modular architecture provides simple mechanisms for scaling out models to meet increased throughput demands and performing fine-grained physical resource allocation for each model. Further, by abstracting models behind a uniform serving interface, Clipper allows developers to compose many machine-learning models within a single application to support increasingly common techniques such as ensemble methods, multi-armed bandit algorithms, and prediction cascades.
In this talk I will provide an overview of the Clipper serving system and discuss how to get started using Clipper to serve Apache Spark and TensorFlow models on Kubernetes. I will then discuss some recent work on statistical performance monitoring for machine learning models.
MLOps with a Feature Store: Filling the Gap in ML Infrastructure
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. The first Feature Stores, developed at hyperscale AI companies such as Uber, Airbnb, and Facebook, enabled feature engineering using domain specific languages, providing abstractions tailored to the companies’ feature engineering domains. However, a general purpose Feature Store needs a general purpose feature engineering, feature selection, and feature transformation platform.
In this talk, we describe how we built a general purpose, open-source Feature Store for ML around dataframes and Apache Spark. We will demonstrate how data engineers can transform and engineers features from backend databases and data lakes, while data scientists can use PySpark to select and transform features into train/test data in a file format of choice (.tfrecords, .npy, .petastorm, etc) on a file system of choice (S3, HDFS). Finally, we will show how the Feature Store enables end-to-end ML pipelines to be factored into feature engineering and data science stages that each can run at different cadences.
Bio:
Fabio Buso is the head of engineering at Logical Clocks AB, where he leads the Feature Store development. Fabio holds a master's degree in cloud computing and services with a focus on data intensive applications, awarded by a joint program between KTH Stockholm and TU Berlin.
Topics: feature store, MLOps.
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The talk I gave at Scale By The Bay.
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How to Productionize Your Machine Learning Models Using Apache Spark MLlib 2....
Apache Spark has rapidly become a key tool for data scientists to explore, understand and transform massive datasets and to build and train advanced machine learning models. The question then becomes, how do you deploy these ML model to a production environment? How do you embed what you’ve learned into customer facing data applications?
In this talk I will discuss best practices on how data scientists productionize machine learning models, do a deep dive with actual case studies, and show live tutorials of a few example architectures and code in Python, Scala, Java and SQL.
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Learn how to use PySpark for processing massive amounts of data. Combined with the GitHub repo - https://github.com/rdempsey/pyspark-for-data-processing - this presentation will help you gain familiarity with processing data using Python and Spark.
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Apache Spark 2.x has laid the foundation for many new features and functionality. Its main three themes—easier, faster, and smarter—are pervasive in its unified and simplified high-level APIs for Structured data.
In this introductory part lecture and part hands-on workshop you’ll learn how to apply some of these new APIs using Databricks Community Edition. In particular, we will cover the following areas:
Apache Spark Fundamentals & Concepts
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SparkSessions vs SparkContexts
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Machine learning has quickly become the hot new tool in the big data ecosystem. Virtually every organization is looking to leverage machine learning and build deeper and richer predictive analytics into their applications.
How does this work though, in practice? What are the challenges organizations run into as they look to move hundreds of models into production? How can they
make the age of both data and models closer to real-time?
This session will focus on how leading practitioners have been able to scale their machine learning deployments in production with the MapR Converged Data Platform.
Use cases that will be featured include autonomous cars and analytics as a service for retail and financial services.
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Deploying machine learning models seems like it should be a relatively easy task. Take your model and pass it some features in production. The reality is that the code written during the prototyping phase of model development doesn’t always work when applied at scale or on “real” data. This talk will explore 1) common problems at the intersection of data science and data engineering 2) how you can structure your code so there is minimal friction between prototyping and production, and 3) how you can use Apache Spark to run predictions on your models in batch or streaming contexts.
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"Databricks Unified Analytics Platform (UAP) is a cloud-based service for running all analytics in one place - from highly reliable and performant data pipelines to state-of-the-art Machine Learning. From the original creators of Apache Spark and MLflow, it provides data science and engineering teams ready to use pre-packaged clusters with optimized Apache Spark and various ML frameworks coupled with powerful collaboration capabilities to improve productivity across the ML lifecycle. Yada yada yada... But in addition to being a vendor Databricks is also a user of UAP.
So, what have we learned by eating our own dogfood? Attend a “from the trenches report” from Suraj Acharya, Director Engineering responsible for Databricks’ in-house data engineering team how his team put Databricks technology to use, the lessons they have learned along the way and best practices for using Databricks for data engineering.
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The best performing offline algorithm can lose in production. The most accurate model does not always improve business metrics. Environment misconfiguration or upstream data pipeline inconsistency can silently kill the model performance. Neither prodops, data science or engineering teams are skilled to detect, monitor and debug such types of incidents.
Was it possible for Microsoft to test Tay chatbot in advance and then monitor and adjust it continuously in production to prevent its unexpected behaviour? Real mission critical AI systems require advanced monitoring and testing ecosystem which enables continuous and reliable delivery of machine learning models and data pipelines into production. Common production incidents include:
Data drifts, new data, wrong features
Vulnerability issues, malicious users
Concept drifts
Model Degradation
Biased Training set / training issue
Performance issue
In this demo based talk we discuss a solution, tooling and architecture that allows machine learning engineer to be involved in delivery phase and take ownership over deployment and monitoring of machine learning pipelines.
It allows data scientists to safely deploy early results as end-to-end AI applications in a self serve mode without assistance from engineering and operations teams. It shifts experimentation and even training phases from offline datasets to live production and closes a feedback loop between research and production.
Technical part of the talk will cover the following topics:
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We’ll also discuss their integration with distributed computing engines such as Apache Spark (which can handle massive amounts of data), as well as help you answer questions such as:
– As a developer how do I pick the right deep learning framework for me?
– Do I want to develop my own model or should I employ an existing one
– How do I strike a trade-off between productivity and control through low-level APIs?
In this session, we will show you how easy it is to build an image classifier with Tensorflow, Keras, and Deep Learning Pipelines in under 30 minutes. After this session, you will walk away with the confidence to evaluate which framework is best for you, and perhaps with a better sense for how to fool an image classifier!
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Machine learning practitioners are most comfortable using high-level programming languages such as Python. This is a barrier to parallelizing algorithms with big data frameworks such as Apache Spark, which are written in lower-level languages. Databricks partnered with the Regeneron Genetics Center to create the Glow library for population-scale genomics data storage and analytics. Glow V1.0.0 includes PySpark-based implementations for both existing and novel machine learning algorithms. We will discuss how leveraging tooling for Python users, especially Pandas UDFs, accelerated our development velocity and impacted our algorithms’ computational performance.
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The cloud has become one of the most attractive ways for enterprises to purchase software, but it requires building products in a very different way from traditional software
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"GOJEK, the Southeast Asian super-app, has seen an explosive growth in both users and data over the past three years. Today the technology startup uses big data powered machine learning to inform decision-making in its ride-hailing, lifestyle, logistics, food delivery, and payment products. From selecting the right driver to dispatch, to dynamically setting prices, to serving food recommendations, to forecasting real-world events. Hundreds of millions of orders per month, across 18 products, are all driven by machine learning.
Building production grade machine learning systems at GOJEK wasn't always easy. Data processing and machine learning pipelines were brittle, long running, and had low reproducibility. Models and experiments were difficult to track, which led to downstream problems in production during serving and model evaluation. In this talk we will cover these and other challenges that we faced while trying to scale end-to-end machine learning systems at GOJEK. We will then introduce MLflow and explore the key features that make it useful as part of an ML platform. Finally, we will show how introducing MLflow into the ML life cycle has helped to solve many of the problems we faced while scaling machine learning at GOJEK.
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In this talk, we present an open-source platform for serving and monitoring models at scale based on Kubeflow’s model serving framework, KFServing, the Hopsworks Online Feature Store for enriching feature vectors with transformer in KFServing, and Spark and Spark Streaming as general purpose frameworks for monitoring models in production.
We also show how Spark Streaming can use the Hopsworks Feature Store to implement continuous data drift detection, where the Feature Store provides statistics on the distribution of feature values in training, and Spark Streaming computes the statistics on live traffic to the model, alerting if the live traffic differs significantly from the training data. We will include a live demonstration of the platform in action.
Simplify Distributed TensorFlow Training for Fast Image Categorization at Sta...
"In addition to the many data engineering initiatives at Starbucks, we are also working on many interesting data science initatives. The business scenarios involved in our deep learning initatives include (but are not limited to) planogram analysis (layout of our stores for efficient partner and customer flow) to predicting product pairings (e.g. purchase a caramel machiato and perhaps you would like caramel brownie) via the product components using graph convolutional networks.
For this session, we will be focusing on how we can run distributed Keras (TensorFlow backend) training to perform image analytics. This will be combined with MLflow to showcase the data science lifecycle and how Databricks + MLflow simplifies it. "
Deploying MLlib for Scoring in Structured Streaming with Joseph Bradley
This talk discusses developments within Apache Spark to allow deployment of MLlib models and pipelines within Structured Streaming jobs. MLlib has proven success and wide adoption for fitting Machine Learning (ML) models on big data. Scalability, expressive Pipeline APIs, and Spark DataFrame integration are key strengths.
Separately, the development of Structured Streaming has provided Spark users with intuitive, performant tools for building Continuous Applications. The smooth integration of batch and streaming APIs and workflows greatly simplifies many production use cases. Given the adoption of MLlib and Structured Streaming in production systems, a natural next step is to combine them: deploy MLlib models and Pipelines for scoring (prediction) in Structured Streaming.
However, before Apache Spark 2.3, many ML Pipelines could not be deployed in streaming. This talk discusses key improvements within MLlib to support streaming prediction. We will discuss currently supported functionality and opportunities for future improvements. With Spark 2.3, almost all MLlib workflows can be deployed for scoring in streaming, and we will demonstrate this live. The ability to deploy full ML Pipelines which include featurization greatly simplifies moving complex ML workflows from development to production. We will also include some discussion of technical challenges, such as featurization via Estimators vs. Transformers and DataFrame column metadata.
Using Spark Mllib Models in a Production Training and Serving Platform: Exper...
Overview Uber's Michelangelo is a machine learning platform that supports training and serving thousands of models in production. Most Michelangelo customer models are based on Spark Mllib. In this talk, we will describe Michelangelo's experiences with and evolving use of Spark Mllib, particularly in the areas of model persistence and online serving. Extended Description Michelangelo [https://eng.uber.com/michelangelo/] was originally developed to support scalable machine learning for production models. Its end-to-end support for scheduled Spark-based data ingestion and model training, along with model evaluation and deployment for batch and online model serving, has gained wide acceptance across Uber. More recently, Michelangelo is evolving to handle more use cases, including evaluating and serving models trained outside of core Michelangelo, e.g., on a distributed tensorflow platform providing Horovod [https://eng.uber.com/horovod/] or using PySpark in a Jupyter notebook on Data Science Workbench [https://eng.uber.com/dsw/] To support evaluation and serving of models trained outside of Michelangelo, Michelangelo's use of Spark Mllib needed updating, to generalize its mechanisms for model persistence and online serving. In this talk, we will describe these mechanisms and explore possible avenues for open-sourcing them.
Speakers: Anne Holler, Michael Mui
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Apache Spark 2.x has laid the foundation for many new features and functionality. Its main three themes—easier, faster, and smarter—are pervasive in its unified and simplified high-level APIs for Structured data.
In this introductory part lecture and part hands-on workshop you’ll learn how to apply some of these new APIs using Databricks Community Edition. In particular, we will cover the following areas:
Apache Spark Fundamentals & Concepts
What’s new in Spark 2.x
SparkSessions vs SparkContexts
Datasets/Dataframes and Spark SQL
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Machine learning has quickly become the hot new tool in the big data ecosystem. Virtually every organization is looking to leverage machine learning and build deeper and richer predictive analytics into their applications.
How does this work though, in practice? What are the challenges organizations run into as they look to move hundreds of models into production? How can they
make the age of both data and models closer to real-time?
This session will focus on how leading practitioners have been able to scale their machine learning deployments in production with the MapR Converged Data Platform.
Use cases that will be featured include autonomous cars and analytics as a service for retail and financial services.
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Deploying machine learning models seems like it should be a relatively easy task. Take your model and pass it some features in production. The reality is that the code written during the prototyping phase of model development doesn’t always work when applied at scale or on “real” data. This talk will explore 1) common problems at the intersection of data science and data engineering 2) how you can structure your code so there is minimal friction between prototyping and production, and 3) how you can use Apache Spark to run predictions on your models in batch or streaming contexts.
You will take away how to address some of productionizing issues that data scientists and data engineers face while deploying machine learning models at scale and a better understanding of how to work collaboratively to minimize disparity between prototyping and productizing.
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"Databricks Unified Analytics Platform (UAP) is a cloud-based service for running all analytics in one place - from highly reliable and performant data pipelines to state-of-the-art Machine Learning. From the original creators of Apache Spark and MLflow, it provides data science and engineering teams ready to use pre-packaged clusters with optimized Apache Spark and various ML frameworks coupled with powerful collaboration capabilities to improve productivity across the ML lifecycle. Yada yada yada... But in addition to being a vendor Databricks is also a user of UAP.
So, what have we learned by eating our own dogfood? Attend a “from the trenches report” from Suraj Acharya, Director Engineering responsible for Databricks’ in-house data engineering team how his team put Databricks technology to use, the lessons they have learned along the way and best practices for using Databricks for data engineering.
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Monitoring AI with AI
Monitoring AI applications with AI
The best performing offline algorithm can lose in production. The most accurate model does not always improve business metrics. Environment misconfiguration or upstream data pipeline inconsistency can silently kill the model performance. Neither prodops, data science or engineering teams are skilled to detect, monitor and debug such types of incidents.
Was it possible for Microsoft to test Tay chatbot in advance and then monitor and adjust it continuously in production to prevent its unexpected behaviour? Real mission critical AI systems require advanced monitoring and testing ecosystem which enables continuous and reliable delivery of machine learning models and data pipelines into production. Common production incidents include:
Data drifts, new data, wrong features
Vulnerability issues, malicious users
Concept drifts
Model Degradation
Biased Training set / training issue
Performance issue
In this demo based talk we discuss a solution, tooling and architecture that allows machine learning engineer to be involved in delivery phase and take ownership over deployment and monitoring of machine learning pipelines.
It allows data scientists to safely deploy early results as end-to-end AI applications in a self serve mode without assistance from engineering and operations teams. It shifts experimentation and even training phases from offline datasets to live production and closes a feedback loop between research and production.
Technical part of the talk will cover the following topics:
Automatic Data Profiling
Anomaly Detection
Clustering of inputs and outputs of the model
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Service Mesh, Envoy Proxy, trafic shadowing
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DevOps and Machine Learning: How do you test and deploy real-time machine learning services given the challenge that machine learning algorithms produce nondeterministic behaviors even for the same input.
ML at the Edge: Building Your Production Pipeline with Apache Spark and Tens...
The explosion of data volume in the years to come challenge the idea of a centralized cloud infrastructure which handles all business needs. Edge computing comes to rescue by pushing the needs of computation and data analysis at the edge of the network, thus avoiding data exchange when makes sense. One of the areas where data exchange could impose a big overhead is scoring ML models especially where data to score are files like images eg. in a computer vision application.
Another concern in some applications, is that of keeping data as private as possible and this is where keeping things local makes sense. In this talk we will discuss current needs and recent advances in model serving, like newly introduced formats for pushing models at the edge nodes eg. mobile phones and how a unified model serving architecture could cover current and future needs for both data scientists and data engineers. This architecture is based among others, on training models in a distributed fashion with TensorFlow and leveraging Spark for cleaning data before training (eg. using TensorFlow connector).
Finally we will describe a microservice based approach for scoring models back at the cloud infrastructure side (where bandwidth can be high) eg. using TensorFlow serving and updating models remotely with a pull model approach for edge devices. We will talk also about implementing the proposed architecture and how that might look on a modern deployment environment eg. Kubernetes.
Data Agility—A Journey to Advanced Analytics and Machine Learning at Scale
This talk will walk you through the typical workflow of a data scientist or a data analyst at Uber, how they get access to Uber's Big data and fast data sources for ad hoc and experimental analysis, how the data platforms will make it easy to discover datasets, run interactive queries against our petabyte scale data lake to identify the features you're interested in, wrangle and prepare data for advanced analytics and machine learning. Our platforms also provide capabilities to do iterative machine learning and deep learning training seamless on single nodes and distributed on our Big data and GPU clusters, analyze, visualize and share the results of their experiments with colleagues and peers to get feedback, and even productionize data analytics jobs and ML models all without a degree in CS. Interested? Come, learn how Uber's Big data platforms and Data science workbench put the power of Spark in the hands of our Data scientists and data analysts for advanced analytics and ML/DL use cases.
Updates from Project Hydrogen: Unifying State-of-the-Art AI and Big Data in A...
"Project Hydrogen is a major Apache Spark initiative to bring state-of-the-art AI and Big Data solutions together. It contains three major projects: 1) barrier execution mode 2) optimized data exchange and 3) accelerator-aware scheduling. A basic implementation of barrier execution mode was merged into Apache Spark 2.4.0, and the community is working on the latter two. In this talk, we will present progress updates to Project Hydrogen and discuss the next steps.
First, we will review the barrier execution mode implementation from Spark 2.4.0. It enables developers to embed distributed training jobs properly on a Spark cluster. We will demonstrate distributed AI integrations built on top it, e.g., Horovod and Distributed TensorFlow. We will also discuss the technical challenges to implement those integrations and future work. Second, we will outline on-going work for optimized data exchange. Its target scenario is distributed model inference. We will present how we do performance testing/profiling, where the bottlenecks are, and how to improve the overall throughput on Spark. If time allows, we might also give updates on accelerator-aware scheduling.
"
Accelerating Deep Learning Training with BigDL and Drizzle on Apache Spark wi...
The BigDL framework scales deep learning for large data sets using Apache Spark. However there is significant scheduling overhead from Spark when running BigDL at large scale. In this talk we propose a new parameter manager implementation that along with coarse-grained scheduling can provide significant speedups for deep learning models like Inception, VGG etc. Aggregation functions like reduce or treeReduce that are used for parameter aggregation in Apache Spark (and the original MapReduce) are slow as the centralized scheduling and driver network bandwidth become a bottleneck especially in large clusters.
To reduce the overhead of parameter aggregation and allow for near-linear scaling, we introduce a new AllReduce operation, a part of the parameter manager in BigDL which is built directly on top of the BlockManager in Apache Spark. AllReduce in BigDL uses a peer-to-peer mechanism to synchronize and aggregate parameters. During parameter synchronization and aggregation, all nodes in the cluster play the same role and driver’s overhead is eliminated thus enabling near-linear scaling. To address the scheduling overhead we use Drizzle, a recently proposed scheduling framework for Apache Spark. Currently, Spark uses a BSP computation model, and notifies the scheduler at the end of each task. Invoking the scheduler at the end of each task adds overheads and results in decreased throughput and increased latency.
Drizzle introduces group scheduling, where multiple iterations (or a group) of iterations are scheduled at once. This helps decouple the granularity of task execution from scheduling and amortizes the costs of task serialization and launch. Finally we will present results from using the new AllReduce operation and Drizzle on a number of common deep learning models including VGG and Inception. Our benchmarks run on Amazon EC2 and Google DataProc will show the speedups and scalability of our implementation.
A Tale of Three Deep Learning Frameworks: TensorFlow, Keras, & Deep Learning ...
We all know what they say – the bigger the data, the better. But when the data gets really big, how do you use it? This talk will cover three of the most popular deep learning frameworks: TensorFlow, Keras, and Deep Learning Pipelines, and when, where, and how to use them.
We’ll also discuss their integration with distributed computing engines such as Apache Spark (which can handle massive amounts of data), as well as help you answer questions such as:
– As a developer how do I pick the right deep learning framework for me?
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Using Databricks as an Analysis Platform
Over the past year, YipitData spearheaded a full migration of its data pipelines to Apache Spark via the Databricks platform. Databricks now empowers its 40+ data analysts to independently create data ingestion systems, manage ETL workflows, and produce meaningful financial research for our clients.
Extending Machine Learning Algorithms with PySpark
Machine learning practitioners are most comfortable using high-level programming languages such as Python. This is a barrier to parallelizing algorithms with big data frameworks such as Apache Spark, which are written in lower-level languages. Databricks partnered with the Regeneron Genetics Center to create the Glow library for population-scale genomics data storage and analytics. Glow V1.0.0 includes PySpark-based implementations for both existing and novel machine learning algorithms. We will discuss how leveraging tooling for Python users, especially Pandas UDFs, accelerated our development velocity and impacted our algorithms’ computational performance.
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Scaling Ride-Hailing with Machine Learning on MLflow
"GOJEK, the Southeast Asian super-app, has seen an explosive growth in both users and data over the past three years. Today the technology startup uses big data powered machine learning to inform decision-making in its ride-hailing, lifestyle, logistics, food delivery, and payment products. From selecting the right driver to dispatch, to dynamically setting prices, to serving food recommendations, to forecasting real-world events. Hundreds of millions of orders per month, across 18 products, are all driven by machine learning.
Building production grade machine learning systems at GOJEK wasn't always easy. Data processing and machine learning pipelines were brittle, long running, and had low reproducibility. Models and experiments were difficult to track, which led to downstream problems in production during serving and model evaluation. In this talk we will cover these and other challenges that we faced while trying to scale end-to-end machine learning systems at GOJEK. We will then introduce MLflow and explore the key features that make it useful as part of an ML platform. Finally, we will show how introducing MLflow into the ML life cycle has helped to solve many of the problems we faced while scaling machine learning at GOJEK.
"
KFServing, Model Monitoring with Apache Spark and a Feature Store
In recent years, MLOps has emerged to bring DevOps processes to the machine learning (ML) development process, aiming at more automation in the execution of repetitive tasks and at smoother interoperability between tools. Among the different stages in the ML lifecycle, model monitoring involves the supervision of model performance over time, involving the combination of techniques in four categories: outlier detection, data drift detection, explainability and adversarial attacks. Most existing model monitoring tools follow a scheduled batch processing approach or analyse model performance using isolated subsets of the inference data. However, for the continuous monitoring of models, stream processing platforms show several advantages, including support for continuous data analytics, scalable processing of large amounts of data and first-class support for window-based aggregations useful for concept drift detection.
In this talk, we present an open-source platform for serving and monitoring models at scale based on Kubeflow’s model serving framework, KFServing, the Hopsworks Online Feature Store for enriching feature vectors with transformer in KFServing, and Spark and Spark Streaming as general purpose frameworks for monitoring models in production.
We also show how Spark Streaming can use the Hopsworks Feature Store to implement continuous data drift detection, where the Feature Store provides statistics on the distribution of feature values in training, and Spark Streaming computes the statistics on live traffic to the model, alerting if the live traffic differs significantly from the training data. We will include a live demonstration of the platform in action.
Simplify Distributed TensorFlow Training for Fast Image Categorization at Sta...
"In addition to the many data engineering initiatives at Starbucks, we are also working on many interesting data science initatives. The business scenarios involved in our deep learning initatives include (but are not limited to) planogram analysis (layout of our stores for efficient partner and customer flow) to predicting product pairings (e.g. purchase a caramel machiato and perhaps you would like caramel brownie) via the product components using graph convolutional networks.
For this session, we will be focusing on how we can run distributed Keras (TensorFlow backend) training to perform image analytics. This will be combined with MLflow to showcase the data science lifecycle and how Databricks + MLflow simplifies it. "
Deploying MLlib for Scoring in Structured Streaming with Joseph Bradley
This talk discusses developments within Apache Spark to allow deployment of MLlib models and pipelines within Structured Streaming jobs. MLlib has proven success and wide adoption for fitting Machine Learning (ML) models on big data. Scalability, expressive Pipeline APIs, and Spark DataFrame integration are key strengths.
Separately, the development of Structured Streaming has provided Spark users with intuitive, performant tools for building Continuous Applications. The smooth integration of batch and streaming APIs and workflows greatly simplifies many production use cases. Given the adoption of MLlib and Structured Streaming in production systems, a natural next step is to combine them: deploy MLlib models and Pipelines for scoring (prediction) in Structured Streaming.
However, before Apache Spark 2.3, many ML Pipelines could not be deployed in streaming. This talk discusses key improvements within MLlib to support streaming prediction. We will discuss currently supported functionality and opportunities for future improvements. With Spark 2.3, almost all MLlib workflows can be deployed for scoring in streaming, and we will demonstrate this live. The ability to deploy full ML Pipelines which include featurization greatly simplifies moving complex ML workflows from development to production. We will also include some discussion of technical challenges, such as featurization via Estimators vs. Transformers and DataFrame column metadata.
Using Spark Mllib Models in a Production Training and Serving Platform: Exper...
Overview Uber's Michelangelo is a machine learning platform that supports training and serving thousands of models in production. Most Michelangelo customer models are based on Spark Mllib. In this talk, we will describe Michelangelo's experiences with and evolving use of Spark Mllib, particularly in the areas of model persistence and online serving. Extended Description Michelangelo [https://eng.uber.com/michelangelo/] was originally developed to support scalable machine learning for production models. Its end-to-end support for scheduled Spark-based data ingestion and model training, along with model evaluation and deployment for batch and online model serving, has gained wide acceptance across Uber. More recently, Michelangelo is evolving to handle more use cases, including evaluating and serving models trained outside of core Michelangelo, e.g., on a distributed tensorflow platform providing Horovod [https://eng.uber.com/horovod/] or using PySpark in a Jupyter notebook on Data Science Workbench [https://eng.uber.com/dsw/] To support evaluation and serving of models trained outside of Michelangelo, Michelangelo's use of Spark Mllib needed updating, to generalize its mechanisms for model persistence and online serving. In this talk, we will describe these mechanisms and explore possible avenues for open-sourcing them.
Speakers: Anne Holler, Michael Mui
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Extending Machine Learning Algorithms with PySpark
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KFServing, Model Monitoring with Apache Spark and a Feature Store
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An Introduction to Sparkling Water by Michal Malohlava
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Deploying MLlib for Scoring in Structured Streaming with Joseph Bradley
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Using Spark Mllib Models in a Production Training and Serving Platform: Exper...
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✅ZERO upfront cost or any extra expenses
✅Risk-Free: 30-Day Money-Back Guarantee!
✅Commercial License included!
See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
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Need for Speed: Removing speed bumps from your Symfony projects ⚡️
No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
Quarkus Hidden and Forbidden Extensions
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
A Sighting of filterA in Typelevel Rite of Passage
Slide deck home: https://fpilluminated.com/deck/220
Globus Compute Introduction - GlobusWorld 2024
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
Globus Connect Server Deep Dive - GlobusWorld 2024
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
Graspan: A Big Data System for Big Code Analysis
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
Navigating the Metaverse: A Journey into Virtual Evolution"
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
Enterprise Software Development with No Code Solutions.pptx
In the ever-evolving landscape of technology, enterprise software development is undergoing a significant transformation. Traditional coding methods are being challenged by innovative no-code solutions, which promise to streamline and democratize the software development process.
This shift is particularly impactful for enterprises, which require robust, scalable, and efficient software to manage their operations. In this article, we will explore the various facets of enterprise software development with no-code solutions, examining their benefits, challenges, and the future potential they hold.
Utilocate provides Smarter, Better, Faster, Safer Locate Ticket Management
Utilocate offers a comprehensive solution for locate ticket management by automating and streamlining the entire process. By integrating with Geospatial Information Systems (GIS), it provides accurate mapping and visualization of utility locations, enhancing decision-making and reducing the risk of errors. The system's advanced data analytics tools help identify trends, predict potential issues, and optimize resource allocation, making the locate ticket management process smarter and more efficient. Additionally, automated ticket management ensures consistency and reduces human error, while real-time notifications keep all relevant personnel informed and ready to respond promptly.
The system's ability to streamline workflows and automate ticket routing significantly reduces the time taken to process each ticket, making the process faster and more efficient. Mobile access allows field technicians to update ticket information on the go, ensuring that the latest information is always available and accelerating the locate process. Overall, Utilocate not only enhances the efficiency and accuracy of locate ticket management but also improves safety by minimizing the risk of utility damage through precise and timely locates.
Mobile App Development Company In Noida | Drona Infotech
Looking for a reliable mobile app development company in Noida? Look no further than Drona Infotech. We specialize in creating customized apps for your business needs.
Visit Us For : https://www.dronainfotech.com/mobile-application-development/
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Spark and machine learning in microservices architecture
- 1. Spark and Machine Learning in Microservices Architecture by Stepan Pushkarev CTO of Hydrosphere.io
- 2. Mission: Accelerate Machine Learning to Production Opensource Products: - Mist: Spark Compute as a Service - ML Lambda: ML Function as a Service - Sonar: Data and ML Monitoring Business Model: Subscription services and hands-on consulting About
- 3. Spark/Kafka Users here? Machine Learning nerds here?
- 4. Stateless Microservices are well studied
- 5. Data, reporting and machine learning architectures are different ● Raw SQL / HiveQL / SQL on Hadoop ● Datawarehouse / Data Lake centric ● Scripts driven ./bin/spark-submit ● Automated with Cron and/or Workflow Managers ● Hosted Notebooks culture ● Traditionally offline / for internal users ● File system aware (HDFS, S3) ● Defined by all-inclusive Hadoop distributions
- 6. - Data Pipelines on Microservices - ML Functions as low latency prediction services Agenda
- 7. Part 1: Data Pipeline Intuition Need to transform Source Data into desired shape
- 8. Evolution: Scripts driven development Python Script SQL Script
- 9. Evolution: More Scripts Python Script SQL Script Python Script SQL Script Python Script SQL Script
- 10. Evolution: Move to Hive/Spark Spark Script Hive Script Spark Script Python Script Hive Script SQL Script
- 12. Typical Python/SQL/Hive/Spark Task 1) Read data 2) Transform 3) Save to temp table for the next task
- 13. Problem: Unmanageable State in Shared folder - Data Flow is not managed. DAG scheduling is a different objection. - Who is responsible for schema migration? Task 1, Task 2 or Manager? - What folder Task 1 should write to and Task 2 should read from? - How to manage folders/resources between parallel sessions? - When / how to clean up shared folder? Another cleanup pipeline? - How to check that data batch is arrived and valid? - How to unit test it? - How to handle errors?
- 14. Statesafe Pipelines 1. Get rid of Workflow Manager! 2. Turn black box tasks and scripts into microservices. 3. Use Avro data contracts between stages. Data is also an API to be standardized, versioned and validated. 4. Segregate black box tasks into (read) (process) and (write) services. 5. Keep the state in shared folder/topic/session manageable by framework rather than data engineers. 6. Abstract engineer from data transport and provide a pure function to deal with.
- 16. Zoom in: Data Transport between stages
- 17. Statesafe Stage
- 18. Zoom in: Spark Stage Details
- 19. Zoom in: Statesafe API for Business Logic
- 20. Zoom in: Statesafe API for Business Logic
- 21. On-demand batch pipelines - Could not be pre-calculated - On-demand parametrized jobs - Interactive - Require large scale processing - Reporting - Simulation (pricing, bank stress testing, taxi rides) - Forecasting (ad campaign, energy savings, others) - Ad-hoc analytics tools for business users
- 22. Bad Practice: Database as API Execute reporting job Mark Job as complete & save result Poll for new tasks Poll for resultSet a flag and parameters to build a report
- 23. Batch prediction / reporting services
- 24. From Vanila Spark to Spark Compute as a Service ./bin/spark-submit - Spark Sessions Pool - REST API Framework - Data API Framework - Infrastructure Integration (EMR, Hortonworks, etc)
- 25. Spark Compute as a Service DEMO
- 26. Part 2: Machine Learning
- 28. Machine Learning: training + serving (deployment)
- 29. pipeline Training (Estimation) pipeline trainpreprocess preprocess
- 31. val test = spark.createDataFrame(Seq( ("spark hadoop"), ("hadoop learning") )).toDF("text") val model = PipelineModel.load("/tmp/spark-model") model.transform(test).collect()
- 33. https://issues.apache.org/jira/browse/SPARK-16365 https://issues.apache.org/jira/browse/SPARK-13944
- 34. cluster data model data scientist web app PMML PFA MLEAP - Yet another Format Lock - Code & state duplication - Limited extensibility - Inconsistency - Extra moving parts
- 35. cluster data model data scientist web app API API - Needs Spark Running - High latency, low throughput
- 36. cluster data model data scientist web app docker API libs model Spark ML Local Serving Library: https://github.com/Hydrospheredata /spark-ml-serving
- 37. Model Container
- 38. Zoo: Models - Runtimes - Standards
- 39. API & Logistics - HTTP/1.1, HTTP/2, gRPC - Kafka, Flink, Kinesis - Protobuf, Avro - Service Discovery - Pipelining - Tracing - Monitoring - Autoscaling - Versioning - A/B, Canary - Testing - CPU, GPU
- 41. UX: Train anywhere and deploy as a Function
- 42. UX: Models and Applications Applications provide public endpoints for the models and compositions of the models.
- 43. UX: Streaming Applications + Batching
- 44. UX: Pipelines, Assembles and BestSLA Applications
- 45. Demo!!!
- 46. Thank you Looking for - Feedback - Advisors, mentors & partners - Pilots and early adopters Stay in touch - @hydrospheredata - https://github.com/Hydrospheredata - https://hydrosphere.io/ - spushkarev@hydrosphere.io