Rental Cars and Industrialized Learning to Rank with Sean Downes
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Data can be viewed as the exhaust of online activity. With the rise of cloud-based data platforms, barriers to data storage and transfer have crumbled. The demand for creative applications and learning from those datasets has accelerated. Rapid acceleration can quickly accrue disorder, and disorderly data design can turn the deepest data lake into an impenetrable swamp. In this talk, I will discuss the evolution of the data science workflow at Expedia with a special emphasis on Learning to Rank problems. From the heroic early days of ad-hoc Spark exploration to our first production sort model on the cloud, we will explore the process of industrializing the workflow. Layered over our story, I will share some best practices and suggestions on how to keep your data productive, or even pull your organization out of the data swamp.
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Apache Superset , apache druid , dataworks summit 19, Apache nifi , apache kafka, apache superset
Bullet: A Real Time Data Query Engine
Bullet is an open sourced, lightweight, pluggable querying system for streaming data without a persistence layer implemented on top of Storm. It allows you to filter, project, and aggregate on data in transit. It includes a UI and WS. Instead of running queries on a finite set of data that arrived and was persisted or running a static query defined at the startup of the stream, our queries can be executed against an arbitrary set of data arriving after the query is submitted. In other words, it is a look-forward system.
Bullet is a multi-tenant system that scales independently of the data consumed and the number of simultaneous queries. Bullet is pluggable into any streaming data source. It can be configured to read from systems such as Storm, Kafka, Spark, Flume, etc. Bullet leverages Sketches to perform its aggregate operations such as distinct, count distinct, sum, count, min, max, and average.
An instance of Bullet is currently running at Yahoo against its user engagement data pipeline. We’ll highlight how it is powering internal use-cases such as web page and native app instrumentation validation. Finally, we’ll show a demo of Bullet and go over query performance numbers.
From Pandas to Koalas: Reducing Time-To-Insight for Virgin Hyperloop's Data
A Databricks Webinar presented by Patrick Oleniuk, Lead Data Engineer and Virgin Hyperloop One and Yifan Cao, Senior Project Manager at Databricks
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Lessons Learned from Managing Thousands of Production Apache Spark Clusters w...
Lessons Learned from Managing Thousands of Production Apache Spark Clusters w...
SSR: Structured Streaming for R and Machine Learning
SSR: Structured Streaming for R and Machine Learning
Using Pluggable Apache Spark SQL Filters to Help GridPocket Users Keep Up wit...
Using Pluggable Apache Spark SQL Filters to Help GridPocket Users Keep Up wit...
Dr. Elephant for Monitoring and Tuning Apache Spark Jobs on Hadoop with Carl ...
Dr. Elephant for Monitoring and Tuning Apache Spark Jobs on Hadoop with Carl ...
The Key to Machine Learning is Prepping the Right Data with Jean Georges Perrin
The Key to Machine Learning is Prepping the Right Data with Jean Georges Perrin
Spark Summit San Francisco 2016 - Ali Ghodsi Keynote
Spark Summit San Francisco 2016 - Ali Ghodsi Keynote
Analytics at Scale with Apache Spark on AWS with Jonathan Fritz
Analytics at Scale with Apache Spark on AWS with Jonathan Fritz
Migrating from Redshift to Spark at Stitch Fix: Spark Summit East talk by Sky...
Migrating from Redshift to Spark at Stitch Fix: Spark Summit East talk by Sky...
Behavior-Driven Development (BDD) Testing with Apache Spark with Aaron Colcor...
Behavior-Driven Development (BDD) Testing with Apache Spark with Aaron Colcor...
Using SparkML to Power a DSaaS (Data Science as a Service) with Kiran Muglurm...
Using SparkML to Power a DSaaS (Data Science as a Service) with Kiran Muglurm...
Introduction to Streaming Distributed Processing with Storm
Introduction to Streaming Distributed Processing with Storm
Developing high frequency indicators using real time tick data on apache supe...
Developing high frequency indicators using real time tick data on apache supe...
From Pandas to Koalas: Reducing Time-To-Insight for Virgin Hyperloop's Data
From Pandas to Koalas: Reducing Time-To-Insight for Virgin Hyperloop's Data
Similar to Rental Cars and Industrialized Learning to Rank with Sean Downes
Big Data made easy in the era of the Cloud - Demi Ben-Ari
Talking about the ease of use and handling Big Data technologies in the Cloud. Using Google Cloud Platform and Amazon Web Services and all of the tools around it.
Showing the problems and how we can solve them with simple tools.
From a student to an apache committer practice of apache io tdb
This talk is introduce by Xiangdong Huang, who is a PPMC of Apache IoTDB (incubating) project, at Apache Event at Tsinghua University in China.
About the Event:
The open source ecosystem plays more and more important role in the world. Open source software is widely used in operating systems, cloud computing, big data, artificial intelligence, and industrial Internet. Many companies have gradually increased their participation in the open source community. Developers with open source experience are increasingly valued and favored by large enterprises. The Apache Software Foundation is one of the most important open source communities, contributing a large number of valuable open source software and communities to the world.
The invited guests of this lecture are all from ASF community, including the chairman of the Apache Software Foundation, three Apache members, Top 5 Apache code committers (according to Apache annual report), the first Committer in the Hadoop project in China, several Apache project mentors or VPs, and many Apache Committers. They will tell you what the open source culture is, how to join the Apache open source community, and the Apache Way.
The Hitchhiker's Guide to Machine Learning with Python & Apache Spark
Presentation for my tutorial at Big Data Tech Con http://goo.gl/4Pcvuq
It is work in progress. I will update with daily snapshot until done.
Searching Chinese Patents Presentation at Enterprise Data World
War stories from building the Global Patent Search Network, and why Data folks need to think more about UX and Discovery, and UX folks need to think more about Data.
Essential Data Engineering for Data Scientist
Here's a special Data Engineering Survival Checklist required to deploy a successful Data Science project.
Paytm labs soyouwanttodatascience
Creating a Data Science Team from an Architect's perspective. This is about team building on how to support a data science team with the right staff, including data engineers and devops.
Data Science with Spark
Slides for the Spark workshop - github https://github.com/xsankar/global-bd-conf
Measure All the Things! - Austin Data Day 2014
Slides used during presentation that covered metrics gathering and analysis
GalvanizeU Seattle: Eleven Almost-Truisms About Data
http://www.meetup.com/Seattle-Data-Science/events/223445403/
Almost a dozen almost-truisms about Data that almost everyone should consider carefully as they embark on a journey into Data Science. There are a number of preconceptions about working with data at scale where the realities beg to differ. This talk estimates that number to be at least eleven, through probably much larger. At least that number has a great line from a movie. Let's consider some of the less-intuitive directions in which this field is heading, along with likely consequences and corollaries -- especially for those who are just now beginning to study about the technologies, the processes, and the people involved.
Data infrastructure architecture for medium size organization: tips for colle...
Data infrastructure architecture for medium size organization: tips for colle...DataWorks Summit/Hadoop Summit
Data infrastructure architecture for medium size organization: tips for collecting, storing and analysis.
(Big) Data (Science) Skills
Keynote presentation about Big Data Science skills presented at the BDVA summit in Madrid (17/06/2015)
From Lab to Factory: Or how to turn data into value
We've all heard of 'big data' or data science, but how do we convert these trends into actual business value. I share case studies, and technology tips and talk about the challenges of the data science process. This is all based on two years of in-the-field research of deploying models, and going from prototypes to production.
These are slides from my talk at PyCon Ireland 2015
Making the Most of In-Memory: More than Speed
The Briefing Room with Robin Bloor and Kognitio
Live Webcast Oct. 1, 2013
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?AT=pb&SP=EC&rID=7539482&rKey=bc304aa8dac7b781
Everyone’s talking about in-memory these days, and the term has become synonymous with speed. But pinning data into memory is just the beginning, and it’s about more than speed. In-memory solutions need a tailored architecture, one that can take full advantage RAM processing from every aspect, and this requires an approach that considers memory and CPU from the ground-up.
Register for this episode of The Briefing Room to hear from veteran Analyst Robin Bloor as he explains how memory is on the fast track to supersede disk, at least with respect to advanced analytics. He’ll be briefed by Kognitio CTO Roger Gaskell, who pioneered the in-memory analytical platform since its inception in 1989. He will also discuss how this type of solution changes the landscape for the modern data architecture and its impact on advanced analytical capabilities.
Visit InsideAnalysis.com for more information
Data Workflows for Machine Learning - SF Bay Area ML
Presented at SF Bay Area ML meetup (2014-04-09)
http://www.meetup.com/SF-Bayarea-Machine-Learning/events/173759442/
Big Graph Analytics on Neo4j with Apache Spark
In this talk I will introduce you to a Docker container that provides you an easy way to do distributed graph processing using Apache Spark GraphX and a Neo4j graph database. You'll learn how to analyze big data graphs that are exported from Neo4j and consequently updated from the results of a Spark GraphX analysis. The types of analysis I will be talking about are PageRank, connected components, triangle counting, and community detection.
Database technologies have evolved to be able to store big data, but are largely inflexible. For complex graph data models stored in a relational database there may be tedious transformations and shuffling around of data to perform large scale analysis.
Fast and scalable analysis of big data has become a critical competitive advantage for companies. There are open source tools like Apache Hadoop and Apache Spark that are providing opportunities for companies to solve these big data problems in a scalable way. Platforms like these have become the foundation of the big data analysis movement.
Speakers
Maintainable Machine Learning Products
Machine learning applications are typically stitched together from hopes and dreams, shell scripts, cron jobs, home-grown schedulers, snippets of configuration clipped from multiple blog posts, thousands of hard-coded business rules, a.k.a. "our SQL corpus," and a few lines of training and testing code. Organizing all the moving parts into something maintainable and supportive of ongoing development is a challenge most teams have on their TODO list, roadmap, or tech debt pile. Getting ahead of the day-to-day demands and settling into a sane architecture often seems like an unattainable goal. The past several years have seen an explosion of tool-building in the data engineering and analytics area, including in Apache projects spanning the areas of search and information retrieval, job orchestration, file and stream formats, and machine learning libraries. In this talk we will cover our product and development teams' choices of architecture and tools, from data ingestion and storage, through transformations and processing, to presentation of results and publishing to web services, reports, and applications.
Architecting a Platform for Enterprise Use - Strata London 2018
The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This session will discuss hidden design assumptions, review design principles to apply when building multi-use data infrastructure, and provide a reference architecture to use as you work to unify your analytics infrastructure.
The focus in our market has been on acquiring technology, and that ignores the more important part: the larger IT landscape within which this technology lives and the data architecture that lies at its core. If one expects longevity from a platform then it should be a designed rather than accidental architecture.
Architecture is more than just software. It starts from use and includes the data, technology, methods of building and maintaining, and organization of people. What are the design principles that lead to good design and a functional data architecture? What are the assumptions that limit older approaches? How can one integrate with, migrate from or modernize an existing data environment? How will this affect an organization's data management practices? This tutorial will help you answer these questions.
Topics covered:
* A brief history of data infrastructure and past design assumptions
* Categories of data and data use in organizations
* Analytic workload characteristics and constraints
* Data architecture
* Functional architecture
* Tradeoffs between different classes of technology
* Technology planning assumptions and guidance
#strataconf
Machine learning model to production
How to bring a machine learning script into a production environment
Similar to Rental Cars and Industrialized Learning to Rank with Sean Downes (20)
Big Data made easy in the era of the Cloud - Demi Ben-Ari
Big Data made easy in the era of the Cloud - Demi Ben-Ari
From a student to an apache committer practice of apache io tdb
From a student to an apache committer practice of apache io tdb
The Hitchhiker's Guide to Machine Learning with Python & Apache Spark
The Hitchhiker's Guide to Machine Learning with Python & Apache Spark
Searching Chinese Patents Presentation at Enterprise Data World
Searching Chinese Patents Presentation at Enterprise Data World
GalvanizeU Seattle: Eleven Almost-Truisms About Data
GalvanizeU Seattle: Eleven Almost-Truisms About Data
Data infrastructure architecture for medium size organization: tips for colle...
Data infrastructure architecture for medium size organization: tips for colle...
From Lab to Factory: Or how to turn data into value
From Lab to Factory: Or how to turn data into value
Data Workflows for Machine Learning - SF Bay Area ML
Data Workflows for Machine Learning - SF Bay Area ML
Architecting a Platform for Enterprise Use - Strata London 2018
Architecting a Platform for Enterprise Use - Strata London 2018
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!
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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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一比一原版(ArtEZ毕业证)ArtEZ艺术学院毕业证成绩单
ArtEZ毕业证【微信95270640】☀《ArtEZ艺术学院毕业证购买》Q微信95270640《ArtEZ毕业证模板办理》文凭、本科、硕士、研究生学历都可以做,《文凭ArtEZ毕业证书原版制作ArtEZ成绩单》《仿制ArtEZ毕业证成绩单ArtEZ艺术学院学位证书pdf电子图》毕业证
[留学文凭学历认证(留信认证使馆认证)ArtEZ艺术学院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ArtEZ艺术学院ArtEZ艺术学院毕业证假文凭信息：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。
2：国家专业人才认证中心颁发入库证书这个入网证书并且可以归档到地方。
3：凡是获得留信网入网的信息将会逐步更新到个人身份内将在公安部网内查询个人身份证信息后同步读取人才网入库信息。
4：个人职称评审加20分个人信誉贷款加10分。
5：在国家人才网主办的全国网络招聘大会中纳入资料供国家500强等高端企业选择人才。却怎么也笑不出来山娃很迷惑父亲的家除了一扇小铁门连窗户也没有墓穴一般阴森森有些骇人父亲的城也便成了山娃的城父亲的家也便成了山娃的家父亲让山娃呆在屋里做作业看电视最多只能在门口透透气不能跟陌生人搭腔更不能乱跑一怕迷路二怕拐子拐人山娃很惊惧去年村里的田鸡就因为跟父亲进城一不小心被人拐跑了至今不见踪影害得田鸡娘天天哭得死去活来疯了一般那情那景无不令人摧肝裂肺山娃很听话天天呆在小屋里除了看书写作业就是睡带
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(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！
如果您是以下情况，我们都能竭诚为您解决实际问题：【公司采用定金+余款的付款流程，以最大化保障您的利益，让您放心无忧】
1、在校期间，因各种原因未能顺利毕业，拿不到官方毕业证+微信95270640
2、面对父母的压力，希望尽快拿到卡尔顿大学卡尔顿大学毕业证文凭证书；
3、不清楚流程以及材料该如何准备卡尔顿大学卡尔顿大学毕业证文凭证书；
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面向美国乔治城大学毕业留学生提供以下服务:
【★卡尔顿大学卡尔顿大学毕业证文凭证书毕业证、成绩单等全套材料，从防伪到印刷，从水印到钢印烫金，与学校100%相同】
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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。
专业服务，请勿犹豫联系我
如果您真实毕业回国，对于学历认证无从下手，请联系我，我们免费帮您递交
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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
你做代理，可以拯救卡尔顿大学失足青年
你做代理，可以挽救卡尔顿大学一个个人才
你做代理，你将是别人人生卡尔顿大学的转折点
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
Malana- Gimlet Market Analysis (Portfolio 2)
A market analysys on Spotify's parent Podcast company Gimlet.
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
CBU毕业证【微信95270640】《如何办理不列颠海角大学毕业证认证》【办证Q微信95270640】《不列颠海角大学文凭毕业证制作》《CBU学历学位证书哪里买》办理不列颠海角大学学位证书扫描件、办理不列颠海角大学雅思证书！
国际留学归国服务中心《如何办不列颠海角大学毕业证认证》《CBU学位证书扫描件哪里买》实体公司，注册经营，行业标杆，精益求精！
1:1完美还原海外各大学毕业材料上的工艺：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。
可办理以下真实不列颠海角大学存档留学生信息存档认证：
1不列颠海角大学真实留信网认证（网上可查永久存档无风险百分百成功入库）；
2真实教育部认证（留服）等一切高仿或者真实可查认证服务（暂时不可办理）；
3购买英美真实学籍（不用正常就读直接出学历）；
4英美一年硕士保毕业证项目（保录取学校挂名不用正常就读保毕业）
留学本科/硕士毕业证书成绩单制作流程：
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不列颠海角大学不列颠海角大学本科学位证成绩单）；
2开始安排制作不列颠海角大学毕业证成绩单电子图；
3不列颠海角大学毕业证成绩单电子版做好以后发送给您确认；
4不列颠海角大学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不列颠海角大学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）
— — — — — — — — — — — 《文凭顾问Q/微：95270640》这么大这么美的地方赚大钱高楼大厦鳞次栉比大街小巷人潮涌动山娃一路张望一路惊叹他发现城里的桥居然层层叠叠扭来扭去桥下没水却有着水一般的车水马龙山娃惊诧于城里的公交车那么大那么美不用买票乖乖地掷下二枚硬币空调享受还能坐着看电视呢屡经辗转山娃终于跟着父亲到家了山娃没想到父亲城里的家会如此寒碜更没料到父亲的城里竟有如此简陋的鬼地方父亲的家在高楼最底屋最下面很矮很黑是很不显眼的地下室父亲的家安在别人脚底下孰
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
一比一原版(CBU毕业证)卡普顿大学毕业证成绩单
CBU毕业证【微信95270640】☀《卡普顿大学毕业证购买》GoogleQ微信95270640《CBU毕业证模板办理》加拿大文凭、本科、硕士、研究生学历都可以做,二、业务范围：
★、全套服务：毕业证、成绩单、化学专业毕业证书伪造《卡普顿大学大学毕业证》Q微信95270640《CBU学位证书购买》
专业为留学生办理卡普顿大学卡普顿大学本科毕业证成绩单【100%存档可查】留学全套申请材料办理。本公司承诺所有毕业证成绩单成品全部按照学校原版工艺对照一比一制作和学校一样的羊皮纸张保证您证书的质量！
如果你回国在学历认证方面有以下难题请联系我们我们将竭诚为你解决认证瓶颈
1所有材料真实但资料不全无法提供完全齐整的原件。【如：成绩单丶毕业证丶回国证明等材料中有遗失的。】
2获得真实的国外最终学历学位但国外本科学历就读经历存在问题或缺陷。【如：国外本科是教育部不承认的或者是联合办学项目教育部没有备案的或者外本科没有正常毕业的。】
3学分转移联合办学等情况复杂不知道怎么整理材料的。时间紧迫自己不清楚递交流程的。
如果你是以上情况之一请联系我们我们将在第一时间内给你免费咨询相关信息。我们将帮助你整理认证所需的各种材料.帮你解决国外学历认证难题。
国外卡普顿大学卡普顿大学本科毕业证成绩单办理方法：
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卡普顿大学卡普顿大学本科毕业证成绩单）；
2开始安排制作卡普顿大学毕业证成绩单电子图；
3卡普顿大学毕业证成绩单电子版做好以后发送给您确认；
4卡普顿大学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卡普顿大学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）。父亲太伟大了居然能单匹马地跑到这么远这么大这么美的地方赚大钱高楼大厦鳞次栉比大街小巷人潮涌动山娃一路张望一路惊叹他发现城里的桥居然层层叠叠扭来扭去桥下没水却有着水一般的车水马龙山娃惊诧于城里的公交车那么大那么美不用买票乖乖地掷下二枚硬币空调享受还能坐着看电视呢屡经辗转山娃终于跟着父亲到家了山娃没想到父亲城里的家会如此寒碜更没料到父亲的城里竟有如此简陋的鬼地方父亲的家在高楼最底屋最下面很矮很黑是子
The affect of service quality and online reviews on customer loyalty in the E...
Journal of Management and Creative Business.published book./
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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三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，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，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
【关于价格问题（保证一手价格）】
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。
Adjusting primitives for graph : SHORT REPORT / NOTES
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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！
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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
哪里卖(usq毕业证书)南昆士兰大学毕业证研究生文凭证书托福证书原版一模一样
原版定制【Q微信:741003700】《(usq毕业证书)南昆士兰大学毕业证研究生文凭证书》【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
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信741003700】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，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，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
【关于价格问题（保证一手价格）】
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(BU毕业证)波士顿大学毕业证成绩单
BU毕业证【微信95270640】购买（波士顿大学毕业证成绩单硕士学历）Q微信95270640代办BU学历认证留信网伪造波士顿大学学位证书精仿波士顿大学本科/硕士文凭证书补办波士顿大学 diplomaoffer,Transcript购买波士顿大学毕业证成绩单购买BU假毕业证学位证书购买伪造波士顿大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
专业为留学生办理波士顿大学波士顿大学毕业证offer【100%存档可查】留学全套申请材料办理。本公司承诺所有毕业证成绩单成品全部按照学校原版工艺对照一比一制作和学校一样的羊皮纸张保证您证书的质量！
如果你回国在学历认证方面有以下难题请联系我们我们将竭诚为你解决认证瓶颈
1所有材料真实但资料不全无法提供完全齐整的原件。【如：成绩单丶毕业证丶回国证明等材料中有遗失的。】
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3学分转移联合办学等情况复杂不知道怎么整理材料的。时间紧迫自己不清楚递交流程的。
如果你是以上情况之一请联系我们我们将在第一时间内给你免费咨询相关信息。我们将帮助你整理认证所需的各种材料.帮你解决国外学历认证难题。
国外波士顿大学波士顿大学毕业证offer办理方法：
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波士顿大学波士顿大学毕业证offer）；
2开始安排制作波士顿大学毕业证成绩单电子图；
3波士顿大学毕业证成绩单电子版做好以后发送给您确认；
4波士顿大学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波士顿大学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）。二条巴掌般大的裤衩衩走出泳池山娃感觉透身粘粘乎乎散发着药水味有点痒山娃顿时留恋起家乡的小河潺潺活水清凉无比日子就这样孤寂而快乐地过着寂寞之余山娃最神往最开心就是晚上无论多晚多累父亲总要携山娃出去兜风逛夜市流光溢彩人潮涌动的都市夜生活总让山娃目不暇接惊叹不已父亲老问山娃想买什么想吃什么山娃知道父亲赚钱很辛苦除了书籍和文具山娃啥也不要能牵着父亲的手满城闲逛他已心满意足了父亲连挑了三套童装叫山娃试穿山伸
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。
2：国家专业人才认证中心颁发入库证书这个入网证书并且可以归档到地方。
3：凡是获得留信网入网的信息将会逐步更新到个人身份内将在公安部网内查询个人身份证信息后同步读取人才网入库信息。
4：个人职称评审加20分个人信誉贷款加10分。
5：在国家人才网主办的全国网络招聘大会中纳入资料供国家500强等高端企业选择人才。广州火车东站的那一刻山娃感受到了一种从未有过的激动和震撼太美了可爱的广州父亲的城山娃惊喜得几乎叫出声来山娃觉得父亲太伟大了居然能单匹马地跑到这么远这么大这么美的地方赚大钱高楼大厦鳞次栉比大街小巷人潮涌动山娃一路张望一路惊叹他发现城里的桥居然层层叠叠扭来扭去桥下没水却有着水一般的车水马龙山娃惊诧于城里的公交车那么大那么美不用买票乖乖地掷下二枚硬币空调享受还能坐着看电视呢屡经辗转山娃终于跟着父亲到拉
Investigate & Recover / StarCompliance.io / Crypto_Crimes
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At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
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Investigate & Recover / StarCompliance.io / Crypto_Crimes
Rental Cars and Industrialized Learning to Rank with Sean Downes
- 1. … and associated Idiosyncratic Operating Principles. Industrializing DataScience Workflows Sean Downes Sr DataScientist @ Expedia, Inc.
- 2. The Problem So you’ve been asked to bring the infrastructure into the cloud. So your Data Lake is actually a Data Swamp.
- 4. Login Impressions Clicks Purchases The Problem Every Line of Business has its own Structure Every MicroService has a log And you want to A|B Test
- 5. Can you please turn… The Problem into using
- 6. Preview Context / Disclaimer Lightning Review of Data Platforms (idiosyncratic) Organizing Principles
- 7. Context / Disclaimer Academic Theoretical Physicist We’ve got some work to do. So… I’m implicitly assuming this talk will be one in an ensemble of opinions
- 8. supercomputers… Lightning Review of Data Platforms
- 9. … the Commerical Data Center… Lightning Review of Data Platforms
- 10. … Virtualized Everything Lightning Review of Data Platforms 1. Assign Tasks their own virtual hardware 2. Expend / Contract Resources by demand 3. Real-time HotSwapping 4. Software Updates Built In 5. Etc Etc Etc.
- 11. idiosyncratic Organizing Principles iOP1) Clarity iOP2) Engineers are not Data Scientists iOP3) PMs are not Data Scientists iOP4) Data Scientists are not Engineers iOP5) Close the Data Loop
- 12. iOP1: Data Clarity PUBLISH THIS INTERNALLY! “big data, big noise” Where is what data? Who owns what field? What is this this field? Where did this field go? Why is this field NULL?
- 13. iOP1: Data Clarity Minibatch streaming into nested JSON? O(10kB)? GZip? O(50-500 MB) Parquet. Snappy. “Expect Data Science” Spark Big Thanks to Jason Pohl @ DB! And Charles Pritchard!
- 14. iOP2: Engineers are not Data Scientists “why would you need to do that?” Scratch Space Cluster Bootstrap Permissions Access S3 Buckets Sandbox Clusters Share Notebooks Across Accounts We DO NOT SPEAK IAM Role/Anything
- 15. iOP2: Engineers are not Data Scientists “why would you need to do that?” if possible: Write your own Pipelines. else: Explain Data Science.
- 16. iOP3: PMs are not Data Scientists “you don’t need that!” Once upon a time in the Flight DataLake… Only 10% of a Search Impression was recorded Worse: It was only the Cheapest10% Many of the bookings where not included in this list!
- 17. iOP4: Data Scientists are Not Engineers “we need to support models in @#&%? format” Pick a Robust Standard and Stick to It. If you’re big enough to worry about this, you can commit code jPMML Everybody Use Git. Now. Yes You. Production Code Matters. Format. Document. Pipelines Count as Production Code.
- 18. iOP5: Close that Data Loop What is your data doing? New Data? Consider Bandits! Big Data? Set up a learning problem. Empower by Design.
- 20. Contact information or call to action goes here. Thank You.