Apache Spark-Based Stratification Library for Machine Learning Use Cases at Netflix with Shiva Chaitanya
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This document describes Netflix's use of data stratification for machine learning experimentation and model training. It discusses how Netflix uses the Boson library and its stratification API to: 1) Downsample datasets while meeting a desired distribution of users based on attributes like country, tenure, number of plays, etc. 2) Allow ML researchers to rapidly iterate on ideas by experimenting on stratified subsets of data offline before testing models online. 3) Ensure models are trained on data that sufficiently represents important user demographics through constraints placed during stratified sampling. The API provides flexible and declarative rules for stratifying user cohorts that inform the library how to sample data rather than specifying implementation details.
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Around the world, businesses are turning to AI to transform the way they operate and serve their customers. But before they can implement these technologies, companies must address the roadblock of moving from batch analytics to making real-time decisions by rapidly accessing and analyzing the relevant information amidst a sea of data. Yaron will explain how to make Spark handle multivariate real-time, historical and event data simultaneously to provide immediate and intelligent responses. He will present several time sensitive use-cases including fraud detection, prevention of outages and customer recommendations to demonstrate how to perform predictive analytics and real-time actions with Spark.
Speaker: Yaron Ekshtein
Clipper: A Low-Latency Online Prediction Serving System: Spark Summit East ta...
Machine learning is being deployed in a growing number of applications which demand real-time, accurate, and robust predictions under heavy query load. However, most machine learning frameworks and systems only address model training and not deployment.
In this talk, we present Clipper, a general-purpose low-latency prediction serving system. Interposing between end-user applications and a wide range of machine learning frameworks, Clipper introduces a modular architecture to simplify model deployment across frameworks. Furthermore, by introducing caching, batching, and adaptive model selection techniques, Clipper reduces prediction latency and improves prediction throughput, accuracy, and robustness without modifying the underlying machine learning frameworks. We evaluated Clipper on four common machine learning benchmark datasets and demonstrate its ability to meet the latency, accuracy, and throughput demands of online serving applications. We also compared Clipper to the Tensorflow Serving system and demonstrate comparable prediction throughput and latency on a range of models while enabling new functionality, improved accuracy, and robustness.
SnappyData, the Spark Database. A unified cluster for streaming, transactions...
Apache Spark 2.0 offers many enhancements that make continuous analytics quite simple. In this talk, we will discuss many other things that you can do with your Apache Spark cluster. We explain how a deep integration of Apache Spark 2.0 and in-memory databases can bring you the best of both worlds! In particular, we discuss how to manage mutable data in Apache Spark, run consistent transactions at the same speed as state-the-art in-memory grids, build and use indexes for point lookups, and run 100x more analytics queries at in-memory speeds. No need to bridge multiple products or manage, tune multiple clusters. We explain how one can take regulation Apache Spark SQL OLAP workloads and speed them up by up to 20x using optimizations in SnappyData.
We then walk through several use-case examples, including IoT scenarios, where one has to ingest streams from many sources, cleanse it, manage the deluge by pre-aggregating and tracking metrics per minute, store all recent data in a in-memory store along with history in a data lake and permit interactive analytic queries at this constantly growing data. Rather than stitching together multiple clusters as proposed in Lambda, we walk through a design where everything is achieved in a single, horizontally scalable Apache Spark 2.0 cluster. A design that is simpler, a lot more efficient, and let’s you do everything from Machine Learning and Data Science to Transactions and Visual Analytics all in one single cluster.
SnappyData at Spark Summit 2017
Why does big data always have to go through a pipeline? multiple data copies, slow, complex and stale analytics? We present a unified analytics platform that brings streaming, transactions and adhoc OLAP style interactive analytics in a single in-memory cluster based on Spark.
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Lessons Learned from Modernizing USCIS Data Analytics Platform
U.S. Citizenship and Immigration Services (USCIS) is the government agency that oversees lawful immigration to the United States.
Enterprise Data Governance and Compliance at Scale with Sri Eshasubbiah and S...
Twilio is a cloud communication platform supporting 40,000+customers, 1+ Million Developers, handling millions of messages per minute across the globe from various different sectors. There are many regulated industries and parts of the world where data needs to be moved, stored and accessed securely. Twilio provides firm foundation for that and is focused towards providing customers a secure and scalable telecommunication cloud platform.
Handling this massive amount of data in secured way is possible because of Kafka and Spark. Twilio’s Data platform team is building a compliance layer on top of Data Pipeline, Data Lake and Bulk Data Transformer to handle different compliance requirements such as GDPR, HIPAA, PCI etc. Secured Data Pipeline is a streaming channel for Data Lake, BI Data Warehouse and Elastic Search whereas Bulk Data Transformer is a ETL channel to transfer and transform bulk data from RDMS. Kafka Connect, Spark SQL and Data frames powers streaming channel and makes data wrangling and de-duping efficient.
The Data Compliance layer has various components such as Data Anonymization, Authentication, Authorization, Auditing, Custom Retention and Data Deletion to handle the requirements of Processor and Controller. Anonymization as a service provides redaction, encryption and data obfuscation and is based on the varying needs of compliance and customers. Role based Access Control is applied on Kafka layers and S3 Layers to make sure only valid systems and users can access the critical data and rest of them will access to have only redacted data. Auditing service tracks all the access to various resources both from processor and controller perspective. Distributed Spark executor model makes the petabytes of data deletion efficient after the custom retention period. Thus scalable, fault-tolerant, distributed, secured, audited data governance pipeline is possible through Kafka, Kafka connect and Spark.
Choose Your Weapon: Comparing Spark on FPGAs vs GPUs
Today, general-purpose CPU clusters are the most widely used environment for data analytics workloads. Recently, acceleration solutions employing field-programmable hardware have emerged providing cost, performance and power consumption advantages. Field programmable gate arrays (FPGAs) and graphics processing units (GPUs) are two leading technologies being applied. GPUs are well-known for high-performance dense-matrix, highly regular operations such as graphics processing and matrix manipulation. FPGAs are flexible in terms of programming architecture and are adept at providing performance for operations that contain conditionals and/or branches. These architectural differences have significant performance impacts, which manifest all the way up to the application layer. It is therefore critical that data scientists and engineers understand these impacts in order to inform decisions about if and how to accelerate.
This talk will characterize the architectural aspects of the two hardware types as applied to analytics, with the ultimate goal of informing the application programmer. Recently, both GPUs and FPGAs have been applied to Apache SparkSQL, via services on Amazon Web Services (AWS) cloud. These solutions’ goal is providing Spark users high performance and cost savings. We first characterize the key aspects of the two hardware platforms. Based on this characterization, we examine and contrast the sets and types of SparkSQL operations they accelerate well, how they accelerate them, and the implications for the user’s application. Finally, we present and analyze a performance comparison of the two AWS solutions (one FPGA-based, one GPU-based). The tests employ the TPC-DS (decision support) benchmark suite, a widely used performance test for data analytics.
Operationalizing Machine Learning—Managing Provenance from Raw Data to Predic...
Our team at Comcast is challenged with operationalizing predictive ML models to improve customer experience. Our goal is to eliminate bottlenecks in the process from model inception to deployment and monitoring.
Traditionally CI/CD manages code and infrastructure artifacts like container definitions. We want to extend it to support granular traceability enabling tracking of ML Models from use-case, to feature/attribute selection, development of versioned datasets, model training code, model evaluation artifacts, model prediction deployment containers, and sinks to which the predictions/outcomes are persisted to. Our framework stack enables us to track models from use-case to deployments, manage and evaluate multiple models simultaneously in the live yet dark mode and continue to monitor models in production against real-world outcomes using configurable policies.
The technologies/components which drive this vision are:
1. FeatureStore – Enables data scientists to reuse versioned features and review feature metrics by models. Self-Service capabilities allow all teams to onboard their events data into the feature store.
2. ModelRepository – Manages meta-data about models including pre-processing parameters (Ex. Scaling parameters for features), mapping to the features needed to execute the model, model discovery mechanisms, etc.
3. Spark on Alluxio – Alluxio provides the universal data plane on top of various under-stores (Ex. S3, HDFS, RDBMS). Apache Spark with its Data Sources API provides a unified query language which Data Scientist use to consume features to create training/validation/test datasets which are versioned and integrated into the full model pipeline using Ground-Context discussed next.
4. Ground-Context – This open-source vendor-neutral data context service enables full traceability from use-case, models, features, model to features mapping, versioned datasets, model training codebase, model deployment containers and prediction/outcome sinks. It integrates with the Feature-Store, Container Repository and Git to integrate data, code and run-time artifacts for CI/CD integration.
Online Security Analytics on Large Scale Video Surveillance System by Yu Cao ...
Spark Summit East Talk
Accelerating Machine Learning on Databricks Runtime
"We all know the unprecedented potential impact for Machine Learning. But how do you take advantage of the myriad of data and ML tools now available? How do you streamline processes, speed up discovery, share knowledge, and scale up implementations for real-life scenarios?
In this talk, we'll cover some of the latest innovations brought into the Databricks Unified Analytics Platform for Machine Learning. In particular we will show you how to:
- Get started quickly using the Databricks Runtime for Machine Learning, that provides pre-configured Databricks clusters including the most popular ML frameworks and libraries, Conda support, performance optimizations, and more.
- Get started with most popular Deep Learning frameworks within a few minutes and go deep with state of the art model DL diagnostics tools.
- Scale up Deep Learning training workloads from a single machine to large clusters for the most demanding applications using the new HorovodRunner with ease.
- How all of these ML frameworks get exposed to large and distributed data using Databricks Runtime for Machine Learning."
Build Large-Scale Data Analytics and AI Pipeline Using RayDP
A large-scale end-to-end data analytics and AI pipeline usually involves data processing frameworks such as Apache Spark for massive data preprocessing, and ML/DL frameworks for distributed training on the preprocessed data. A conventional approach is to use two separate clusters and glue multiple jobs. Other solutions include running deep learning frameworks in an Apache Spark cluster, or use workflow orchestrators like Kubeflow to stitch distributed programs. All these options have their own limitations. We introduce Ray as a single substrate for distributed data processing and machine learning. We also introduce RayDP which allows you to start an Apache Spark job on Ray in your python program and utilize Ray’s in-memory object store to efficiently exchange data between Apache Spark and other libraries. We will demonstrate how this makes building an end-to-end data analytics and AI pipeline simpler and more efficient.
MLflow: Infrastructure for a Complete Machine Learning Life Cycle
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.
In this talk, we will present MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
Using SparkML to Power a DSaaS (Data Science as a Service): Spark Summit East...
Almost all organizations now have a need for datascience and as such the main challenge after determining the algorithm is to scale it up and make it operational. We at comcast use several tools and technologies such as Python, R, SaS, H2O and so on.
In this talk we will show how many common use cases use the common algorithms like Logistic Regression, Random Forest, Decision Trees , Clustering, NLP etc.
Spark has several Machine Learning algorithms built in and has excellent scalability. Hence we at comcast built a platform to provide DSaaS on top of Spark with REST API as a means of controlling and submitting jobs so as to abstract most users from the rigor of writing(repeating ) code instead focusing on the actual requirements. We will show how we solved some of the problems of establishing feature vectors, choosing algorithms and then deploying models into production.
We will showcase our use of Scala, R and Python to implement models using language of choice yet deploying quickly into production on 500 node Spark clusters.
A Predictive Analytics Workflow on DICOM Images using Apache Spark with Anahi...
In healthcare, DICOM is an international standard format for storing medical images (MRI/CT representations). Each image has associated with it embedded metadata and pixel data. There is currently a tremendous amount of effort in healthcare to incorporate image analytics within clinical data analysis. Apache Spark is a natural framework to integrate these efforts.
This session presents an analytics workflow using Apache Spark to perform ETL on DICOM images, and then to perform Eigen decomposition to derive meaningful insights on the pixel data. The workflow integrates a Java based framework DCM4CHE with Apache Spark to parallelize the big data workload for fast processing. Users can extract features based on the metadata and run efficient clean/filter/drill-down for preprocessing. See a demonstration of predictive analytics with visualization using the metadata to derive insights, such as likelihood of a condition or efficacy of medication administered.
The speakers will also present performance benchmarks of this workflow on various datasets and cluster configurations to demonstrate the benefits of running this kind of analysis workflow on Apache Spark.
How to Boost 100x Performance for Real World Application with Apache Spark-(G...
Presentation at Spark Summit 2015
Going Real-Time: Creating Frequently-Updating Datasets for Personalization: S...
Streaming applications have often been complex to design and maintain because of the significant upfront infrastructure investment required. However, with the advent of Spark an easy transition to stream processing is now available, enabling personalization applications and experiments to consume near real-time data without massive development cycles.
Our decision to evaluate Spark as our stream processing engine was primarily led by the following considerations: 1) Ease of development for the team (already familiar with spark for batch), 2) the scope/requirements of our problem, 3) re-usability of code from spark batch jobs, and 4) Spark support from infrastructure teams within the company.
In this session, we will present our experience using Spark for stream processing unbounded datasets in the personalization space. The datasets consisted of, but were not limited, to the stream of playback events that are used as feedback for all personalization algorithms. These plays are used to extract specific behaviors which are highly predictive of a customer’s enjoyment of our service. This dataset is massive and has to be further enriched by other online and offline Netflix data sources. These datasets, when consumed by our machine learning models, directly affect the customer’s personalized experience, which means that the impact is high and tolerance for failure is low. We’ll talk about the experiments we did to compare Spark with other streaming solutions like Apache Flink , the impact that we had on our customers, and most importantly, the challenges we faced.
Take-aways for the audience:
1) A great example of stream processing large, personalization datasets at scale.
2) An increased awareness of the costs/requirements for making the transition from batch to streaming successfully.
3) Exposure to some of the technical challenges that should be expected along the way.
Cloud-Native Apache Spark Scheduling with YuniKorn Scheduler
Kubernetes is the most popular container orchestration system that is natively designed for Cloud. At Lyft and Cloudera, we have both emerged the next-generation, cloud-native infrastructure based on Kubernetes, which supports various distributed workloads.
Apache Spark At Scale in the Cloud
Using Apache Spark to analyze large datasets in the cloud presents a range of challenges. Different stages of your pipeline may be constrained by CPU, memory, disk and/or network IO. But what if all those stages have to run on the same cluster? In the cloud, you have limited control over the hardware your cluster runs on.
You may have even less control over the size and format of your raw input files. Performance tuning is an iterative and experimental process. It’s frustrating with very large datasets: what worked great with 30 billion rows may not work at all with 400 billion rows. But with strategic optimizations and compromises, 50+ TiB datasets can be no big deal.
By using Spark UI and simple metrics, explore how to diagnose and remedy issues on jobs:
Sizing the cluster based on your dataset (shuffle partitions)
Ingestion challenges – well begun is half done (globbing S3, small files)
Managing memory (sorting GC – when to go parallel, when to go G1, when offheap can help you)
Shuffle (give a little to get a lot – configs for better out of box shuffle) – Spill (partitioning for the win)
Scheduling (FAIR vs FIFO, is there a difference for your pipeline?)
Caching and persistence (it’s the cost of doing business, so what are your options?)
Fault tolerance (blacklisting, speculation, task reaping)
Making the best of a bad deal (skew joins, windowing, UDFs, very large query plans)
Writing to S3 (dealing with write partitions, HDFS and s3DistCp vs writing directly to S3)
Tactical Data Science Tips: Python and Spark Together
Running Spark and Python data science workloads can be challenging given the complexity of the various data science tools in the ecosystem like sci-kit Learn, TensorFlow, Spark, Pandas, and MLlib. All these various tools and architectures, provide important trade-offs to consider when it comes to moving to proofs of concept and going to production. While proof of concepts may be relatively straightforward, moving to production can be challenging because it’s difficult to understand not just the short term effort to develop a solution, but the long term cost of supporting projects over the long term.
This talk will discuss important tactical patterns for evaluating projects, running proofs of concept to inform going to production, and finally the key tactics we use internally at Databricks to take data and machine learning projects into production. This session will cover some architectural choices involving Spark, PySpark, Pandas, notebooks, various machine learning toolkits, as well as frameworks and technologies necessary to support them.
Stream, Stream, Stream: Different Streaming Methods with Apache Spark and Kafka
At NMC (Nielsen Marketing Cloud) we provide our customers (marketers and publishers) real-time analytics tools to profile their target audiences. To achieve that, we need to ingest billions of events per day into our big data stores, and we need to do it in a scalable yet cost-efficient manner.
In this session, we will discuss how we continuously transform our data infrastructure to support these goals. Specifically, we will review how we went from CSV files and standalone Java applications all the way to multiple Kafka and Spark clusters, performing a mixture of Streaming and Batch ETLs, and supporting 10x data growth We will share our experience as early-adopters of Spark Streaming and Spark Structured Streaming, and how we overcame technical barriers (and there were plenty). We will present a rather unique solution of using Kafka to imitate streaming over our Data Lake, while significantly reducing our cloud services’ costs. Topics include:
Kafka and Spark Streaming for stateless and stateful use-cases
Spark Structured Streaming as a possible alternative
Combining Spark Streaming with batch ETLs
”Streaming” over Data Lake using Kafka
Scalable Monitoring Using Apache Spark and Friends with Utkarsh Bhatnagar
This session will give a new dimension to Apache Spark’s usage. See how Apache Spark and other open source projects can be used together in providing a scalable, real-time monitoring system. Apache Spark plays the central role in providing this scalable solution, since without Spark Streaming we would not be able to process millions of events in real time. This approach can provide a lot of learning to the DevOps/Infrastructure domain on how to build a scalable and automated logging and monitoring solution using Apache Spark, Apache Kafka, Grafana and some other open-source technologies.
Sony PlayStation’s monitoring pipeline processes about 40 billion events every day, and generates metrics in near real-time (within 30 seconds). All the components, used along with Apache Spark, are horizontally scalable using any auto-scaling techniques, which enhances the reliability of this efficient and highly available monitoring solution. Sony Interactive Entertainment has been using Apache Spark, and specifically Spark Streaming, for the last three years. Hear about some important lessons they have learned. For example, they still use Spark Streaming’s receiver-based method in certain use cases instead of Direct Streaming, and will share the application of both the methods, giving the knowledge back to the community.
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.
Real-Time Analytics and Actions Across Large Data Sets with Apache Spark
Around the world, businesses are turning to AI to transform the way they operate and serve their customers. But before they can implement these technologies, companies must address the roadblock of moving from batch analytics to making real-time decisions by rapidly accessing and analyzing the relevant information amidst a sea of data. Yaron will explain how to make Spark handle multivariate real-time, historical and event data simultaneously to provide immediate and intelligent responses. He will present several time sensitive use-cases including fraud detection, prevention of outages and customer recommendations to demonstrate how to perform predictive analytics and real-time actions with Spark.
Speaker: Yaron Ekshtein
Clipper: A Low-Latency Online Prediction Serving System: Spark Summit East ta...
Machine learning is being deployed in a growing number of applications which demand real-time, accurate, and robust predictions under heavy query load. However, most machine learning frameworks and systems only address model training and not deployment.
In this talk, we present Clipper, a general-purpose low-latency prediction serving system. Interposing between end-user applications and a wide range of machine learning frameworks, Clipper introduces a modular architecture to simplify model deployment across frameworks. Furthermore, by introducing caching, batching, and adaptive model selection techniques, Clipper reduces prediction latency and improves prediction throughput, accuracy, and robustness without modifying the underlying machine learning frameworks. We evaluated Clipper on four common machine learning benchmark datasets and demonstrate its ability to meet the latency, accuracy, and throughput demands of online serving applications. We also compared Clipper to the Tensorflow Serving system and demonstrate comparable prediction throughput and latency on a range of models while enabling new functionality, improved accuracy, and robustness.
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Lessons Learned from Modernizing USCIS Data Analytics Platform
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Enterprise Data Governance and Compliance at Scale with Sri Eshasubbiah and S...
Choose Your Weapon: Comparing Spark on FPGAs vs GPUs
Choose Your Weapon: Comparing Spark on FPGAs vs GPUs
Operationalizing Machine Learning—Managing Provenance from Raw Data to Predic...
Operationalizing Machine Learning—Managing Provenance from Raw Data to Predic...
Online Security Analytics on Large Scale Video Surveillance System by Yu Cao ...
Online Security Analytics on Large Scale Video Surveillance System by Yu Cao ...
Accelerating Machine Learning on Databricks Runtime
Accelerating Machine Learning on Databricks Runtime
Build Large-Scale Data Analytics and AI Pipeline Using RayDP
Build Large-Scale Data Analytics and AI Pipeline Using RayDP
MLflow: Infrastructure for a Complete Machine Learning Life Cycle
MLflow: Infrastructure for a Complete Machine Learning Life Cycle
Using SparkML to Power a DSaaS (Data Science as a Service): Spark Summit East...
Using SparkML to Power a DSaaS (Data Science as a Service): Spark Summit East...
A Predictive Analytics Workflow on DICOM Images using Apache Spark with Anahi...
A Predictive Analytics Workflow on DICOM Images using Apache Spark with Anahi...
How to Boost 100x Performance for Real World Application with Apache Spark-(G...
How to Boost 100x Performance for Real World Application with Apache Spark-(G...
Going Real-Time: Creating Frequently-Updating Datasets for Personalization: S...
Going Real-Time: Creating Frequently-Updating Datasets for Personalization: S...
Cloud-Native Apache Spark Scheduling with YuniKorn Scheduler
Cloud-Native Apache Spark Scheduling with YuniKorn Scheduler
Tactical Data Science Tips: Python and Spark Together
Tactical Data Science Tips: Python and Spark Together
Stream, Stream, Stream: Different Streaming Methods with Apache Spark and Kafka
Stream, Stream, Stream: Different Streaming Methods with Apache Spark and Kafka
Scalable Monitoring Using Apache Spark and Friends with Utkarsh Bhatnagar
Scalable Monitoring Using Apache Spark and Friends with Utkarsh Bhatnagar
Extending Machine Learning Algorithms with PySpark
Extending Machine Learning Algorithms with PySpark
Real-Time Analytics and Actions Across Large Data Sets with Apache Spark
Real-Time Analytics and Actions Across Large Data Sets with Apache Spark
Clipper: A Low-Latency Online Prediction Serving System: Spark Summit East ta...
Clipper: A Low-Latency Online Prediction Serving System: Spark Summit East ta...
Similar to Apache Spark-Based Stratification Library for Machine Learning Use Cases at Netflix with Shiva Chaitanya
SnappyData, the Spark Database. A unified cluster for streaming, transactions...
Apache Spark 2.0 offers many enhancements that make continuous analytics quite simple. In this talk, we will discuss many other things that you can do with your Apache Spark cluster. We explain how a deep integration of Apache Spark 2.0 and in-memory databases can bring you the best of both worlds! In particular, we discuss how to manage mutable data in Apache Spark, run consistent transactions at the same speed as state-the-art in-memory grids, build and use indexes for point lookups, and run 100x more analytics queries at in-memory speeds. No need to bridge multiple products or manage, tune multiple clusters. We explain how one can take regulation Apache Spark SQL OLAP workloads and speed them up by up to 20x using optimizations in SnappyData.
We then walk through several use-case examples, including IoT scenarios, where one has to ingest streams from many sources, cleanse it, manage the deluge by pre-aggregating and tracking metrics per minute, store all recent data in a in-memory store along with history in a data lake and permit interactive analytic queries at this constantly growing data. Rather than stitching together multiple clusters as proposed in Lambda, we walk through a design where everything is achieved in a single, horizontally scalable Apache Spark 2.0 cluster. A design that is simpler, a lot more efficient, and let’s you do everything from Machine Learning and Data Science to Transactions and Visual Analytics all in one single cluster.
SnappyData at Spark Summit 2017
Why does big data always have to go through a pipeline? multiple data copies, slow, complex and stale analytics? We present a unified analytics platform that brings streaming, transactions and adhoc OLAP style interactive analytics in a single in-memory cluster based on Spark.
Predicting Optimal Parallelism for Data Analytics
A key benefit of serverless computing is that resources can be allocated on demand, but the quantity of resources to request, and allocate, for a job can profoundly impact its running time and cost. For a job that has not yet run, how can we provide users with an estimate of how the job’s performance changes with provisioned resources, so that users can make an informed choice upfront about cost-performance tradeoffs?
This talk will describe several related research efforts at Microsoft to address this question. We focus on optimizing the amount of computational resources that control a data analytics query’s achieved intra-parallelism. These use machine learning models on query characteristics to predict the run time or Performance Characteristic Curve (PCC) as a function of the maximum parallelism that the query will be allowed to exploit.
The AutoToken project uses models to predict the peak number of tokens (resource units) that is determined by the maximum parallelism that the recurring SCOPE job can ever exploit while running in Cosmos, an Exascale Big Data analytics platform at Microsoft. AutoToken_vNext, or TASQ, predicts the PCC as a function of the number of allocated tokens (limited parallelism). The AutoExecutor project uses models to predict the PCC for Apache Spark SQL queries as a function of the number of executors. The AutoDOP project uses models to predict the run time for SQL Server analytics queries, running on a single machine, as a function of their maximum allowed Degree Of Parallelism (DOP).
We will present our approaches and prediction results for these scenarios, discuss some common challenges that we handled, and outline some open research questions in this space.
Azure CosmosDb - Where we are
Introduce (once again) CosmosDb, comparing with common habit of relational SQL database and try to speak about do and don't with it
Time Series Analytics Azure ADX
Let's make a brief introduction to Azure Data eXplorer, with many examples using Kusto dialect and C# client.
With a particular focus on IIoT contexts and proces control data, let's discover how to implement time series analysis in terms of pattern recognition, and trend correlation.
Learnings Using Spark Streaming and DataFrames for Walmart Search: Spark Summ...
In this presentation, we are going to talk about the state of the art infrastructure we have established at Walmart Labs for the Search product using Spark Streaming and DataFrames. First, we have been able to successfully use multiple micro batch spark streaming pipelines to update and process information like product availability, pick up today etc. along with updating our product catalog information in our search index to up to 10,000 kafka events per sec in near real-time. Earlier, all the product catalog changes in the index had a 24 hour delay, using Spark Streaming we have made it possible to see these changes in near real-time. This addition has provided a great boost to the business by giving the end-costumers instant access to features likes availability of a product, store pick up, etc.
Second, we have built a scalable anomaly detection framework purely using Spark Data Frames that is being used by our data pipelines to detect abnormality in search data. Anomaly detection is an important problem not only in the search domain but also many domains such as performance monitoring, fraud detection, etc. During this, we realized that not only are Spark DataFrames able to process information faster but also are more flexible to work with. One could write hive like queries, pig like code, UDFs, UDAFs, python like code etc. all at the same place very easily and can build DataFrame template which can be used and reused by multiple teams effectively. We believe that if implemented correctly Spark Data Frames can potentially replace hive/pig in big data space and have the potential of becoming unified data language.
We conclude that Spark Streaming and Data Frames are the key to processing extremely large streams of data in real-time with ease of use.
MLlib: Spark's Machine Learning Library
"MLlib: Spark's Machine Learning Library" by Ameet Talwalkar at AMPCamp 5
Intro to SnappyData Webinar
The slides for the first ever SnappyData webinar. Covers SnappyData core concepts, programming models, benchmarks and more.
SnappyData is open sourced here: https://github.com/SnappyDataInc/snappydata
We also have a deep technical paper here: http://www.snappydata.io/snappy-industrial
We can be easily contacted on Slack, Gitter and more: http://www.snappydata.io/about#contactus
SnappyData Ad Analytics Use Case -- BDAM Meetup Sept 14th
Presented at the BDAM meetup in Palo Alto on Sept 14th. Jags Ramnarayan, CTO, SnappyData, discusses an ad analytics use case running on SnappyData.
Explore big data at speed of thought with Spark 2.0 and Snappydata
Abstract:
Data exploration often requires running aggregation/slice-dice queries on data sourced from disparate sources. You may want to identify distribution patterns, outliers, etc and aid the feature selection process as you train your predictive models. As you begin to understand your data, you want to ask ad-hoc questions expressed through your visualization tool (which typically translates to SQL queries), study the results and iteratively explore the data set through more queries. Unfortunately, even when data sets can be in-memory, large data set computations take time breaking the train of thought and increasing time to insight . We know Spark can be fast through its in-memory parallel processing. But, Spark 1.x isn’t quite there. Spark 2.0 promises to offer 10X better speed than its predecessor. Spark 2.0 ushers some impressive improvements to interactive query performance. We first explore these advances - compiling the query plan eliminating virtual function calls, and other improvements in the Catalyst engine. We compare the performance to other popular popular query processing engines by studying the spark query plans. We then go through SnappyData (an open source project that integrates Spark with a database that offers OLTP, OLAP and stream processing in a single cluster) where we use smarter data colocation and Synopses data (.e.g. Stratified sampling) to dramatically cut down on the memory requirements as well as the query latency. We explain the key concepts in summarizing data using structures like stratified sampling by walking through some examples in Apache Zeppelin notebooks (a open source visualization tool for spark) and demonstrate how we can explore massive data sets with just your laptop resources while achieving remarkable speeds.
Bio:
Jags is a founder and the CTO of SnappyData. Previously, Jags was the Chief Architect for “fast data” products at Pivotal and served in the extended leadership team of the company. At Pivotal and previously at VMWare, he led the technology direction for GemFire and other distributed in-memory Bio:
Jags Ramnarayan is a founder and the CTO of SnappyData. Previously, Jags was the Chief Architect for “fast data” products at Pivotal and served in the extended leadership team of the company. At Pivotal and previously at VMWare, he led the technology direction for GemFire and other distributed in-memory products.
Javantura v4 - Java and lambdas and streams - are they better than for loops ...
Javantura v4 - Java and lambdas and streams - are they better than for loops ...HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association
Javantura v4 - Java and lambdas and streams - are they better than for loops - Aleksander Radovan & Branko MihaljevićDeepLearning001&ApacheMXNetWithSparkForInference-ACNA2018
What is Deep Learning
Rise of Deep Learning
Phases of Deep Learning - Training and Inference
AI & Limitations of Deep Learning
Apache MXNet History, Apache MXNet concepts
How to use Apache MXNet and Spark together for Distributed Inference.
The Analytics Frontier of the Hadoop Eco-System
In this video from the ISC Big Data'14 Conference, Ted Willke from Intel presents: The Analytics Frontier of the Hadoop Eco-System.
"The Hadoop MapReduce framework grew out of an effort to make it easy to express and parallelize simple computations that were routinely performed at Google. It wasn’t long before libraries, like Apache Mahout, were developed to enable matrix factorization, clustering, regression, and other more complex analyses on Hadoop. Now, many of these libraries and their workloads are migrating to Apache Spark because it supports a wider class of applications than MapReduce and is more appropriate for iterative algorithms, interactive processing, and streaming applications. What’s next beyond Spark? Where is big data analytics processing headed? How will data scientists program these systems? In this talk, we will explore the current analytics frontier, the popular debates, and discuss some potentially clever additions. We will also share the emergent data science applications and collaborative university research that inform our thinking."
Learn more:
http://www.isc-events.com/bigdata14/schedule.html
and
http://www.intel.com/content/www/us/en/software/intel-graph-solutions.html
Watch the video presentation: https://www.youtube.com/watch?v=qlfx495Ekw0
Machine Learning with ML.NET and Azure - Andy Cross
ML.NET is an open source, machine learning framework built in .NET and runs on Windows, Linux and macOS. It allows developers to integrate custom machine learning into their applications without any prior expertise in developing or tuning machine learning models. Enhance your .NET apps with sentiment analysis, price prediction, fraud detection and more using custom models built with ML.NET
About Andy Cross
Andy Cross (@andyelastacloud) is a co-founder of Elastacloud, an Azure Insider, co-founder of the UK London Azure User Group, an Azure MVP and a Microsoft Regional Director. An international speaker, Andy has lead teams building the largest Hadoop and HDInsight specialist deployments on Azure.
His passion for embedded software and high performance compute clusters gives him a unique insight into a sphere of computation from the very small and resource constrained to the massively scalable, limitless potential of the cloud.
Vectorized Deep Learning Acceleration from Preprocessing to Inference and Tra...
This talk presents how we accelerated deep learning processing from preprocessing to inference and training on Apache Spark in SK Telecom. In SK Telecom, we have half a Korean population as our customers. To support them, we have 400,000 cell towers, which generates logs with geospatial tags.
Time Series Databases for IoT (On-premises and Azure)
Devices from the IoT realm generate data in a rate and magnitude that make it practically impossible to retrieve valuable information without support of adequate AI engines.
Storing and serving billions of data measurements over time is also a non-trivial task addressed by the special class of Time Series DBs. Out of these, InfluxDB has the largest popularity, provides comprehensive documentation and above all - is available open source. As well Microsoft have recently released Azure Time Series Insights - cloud offering of a TS DB with the usability promises from the Microsoft brand.
This session is about managing and understanding IoT data.
Similar to Apache Spark-Based Stratification Library for Machine Learning Use Cases at Netflix with Shiva Chaitanya (20)
SnappyData, the Spark Database. A unified cluster for streaming, transactions...
SnappyData, the Spark Database. A unified cluster for streaming, transactions...
Learnings Using Spark Streaming and DataFrames for Walmart Search: Spark Summ...
Learnings Using Spark Streaming and DataFrames for Walmart Search: Spark Summ...
SnappyData Ad Analytics Use Case -- BDAM Meetup Sept 14th
SnappyData Ad Analytics Use Case -- BDAM Meetup Sept 14th
Explore big data at speed of thought with Spark 2.0 and Snappydata
Explore big data at speed of thought with Spark 2.0 and Snappydata
Javantura v4 - Java and lambdas and streams - are they better than for loops ...
Javantura v4 - Java and lambdas and streams - are they better than for loops ...
DeepLearning001&ApacheMXNetWithSparkForInference-ACNA2018
DeepLearning001&ApacheMXNetWithSparkForInference-ACNA2018
Lessons learned from embedding Cassandra in xPatterns
Lessons learned from embedding Cassandra in xPatterns
Machine Learning with ML.NET and Azure - Andy Cross
Machine Learning with ML.NET and Azure - Andy Cross
Vectorized Deep Learning Acceleration from Preprocessing to Inference and Tra...
Vectorized Deep Learning Acceleration from Preprocessing to Inference and Tra...
Time Series Databases for IoT (On-premises and Azure)
Time Series Databases for IoT (On-premises and Azure)
More from Databricks
Data Lakehouse Symposium | Day 1 | Part 1
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Data Lakehouse Symposium | Day 1 | Part 2
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Data Lakehouse Symposium | Day 2
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Data Lakehouse Symposium | Day 4
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
In this session, learn how to quickly supplement your on-premises Hadoop environment with a simple, open, and collaborative cloud architecture that enables you to generate greater value with scaled application of analytics and AI on all your data. You will also learn five critical steps for a successful migration to the Databricks Lakehouse Platform along with the resources available to help you begin to re-skill your data teams.
Democratizing Data Quality Through a Centralized Platform
Bad data leads to bad decisions and broken customer experiences. Organizations depend on complete and accurate data to power their business, maintain efficiency, and uphold customer trust. With thousands of datasets and pipelines running, how do we ensure that all data meets quality standards, and that expectations are clear between producers and consumers? Investing in shared, flexible components and practices for monitoring data health is crucial for a complex data organization to rapidly and effectively scale.
At Zillow, we built a centralized platform to meet our data quality needs across stakeholders. The platform is accessible to engineers, scientists, and analysts, and seamlessly integrates with existing data pipelines and data discovery tools. In this presentation, we will provide an overview of our platform’s capabilities, including:
Giving producers and consumers the ability to define and view data quality expectations using a self-service onboarding portal
Performing data quality validations using libraries built to work with spark
Dynamically generating pipelines that can be abstracted away from users
Flagging data that doesn’t meet quality standards at the earliest stage and giving producers the opportunity to resolve issues before use by downstream consumers
Exposing data quality metrics alongside each dataset to provide producers and consumers with a comprehensive picture of health over time
Learn to Use Databricks for Data Science
Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
Why APM Is Not the Same As ML Monitoring
Application performance monitoring (APM) has become the cornerstone of software engineering allowing engineering teams to quickly identify and remedy production issues. However, as the world moves to intelligent software applications that are built using machine learning, traditional APM quickly becomes insufficient to identify and remedy production issues encountered in these modern software applications.
As a lead software engineer at NewRelic, my team built high-performance monitoring systems including Insights, Mobile, and SixthSense. As I transitioned to building ML Monitoring software, I found the architectural principles and design choices underlying APM to not be a good fit for this brand new world. In fact, blindly following APM designs led us down paths that would have been better left unexplored.
In this talk, I draw upon my (and my team’s) experience building an ML Monitoring system from the ground up and deploying it on customer workloads running large-scale ML training with Spark as well as real-time inference systems. I will highlight how the key principles and architectural choices of APM don’t apply to ML monitoring. You’ll learn why, understand what ML Monitoring can successfully borrow from APM, and hear what is required to build a scalable, robust ML Monitoring architecture.
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
Autonomy and ownership are core to working at Stitch Fix, particularly on the Algorithms team. We enable data scientists to deploy and operate their models independently, with minimal need for handoffs or gatekeeping. By writing a simple function and calling out to an intuitive API, data scientists can harness a suite of platform-provided tooling meant to make ML operations easy. In this talk, we will dive into the abstractions the Data Platform team has built to enable this. We will go over the interface data scientists use to specify a model and what that hooks into, including online deployment, batch execution on Spark, and metrics tracking and visualization.
Stage Level Scheduling Improving Big Data and AI Integration
In this talk, I will dive into the stage level scheduling feature added to Apache Spark 3.1. Stage level scheduling extends upon Project Hydrogen by improving big data ETL and AI integration and also enables multiple other use cases. It is beneficial any time the user wants to change container resources between stages in a single Apache Spark application, whether those resources are CPU, Memory or GPUs. One of the most popular use cases is enabling end-to-end scalable Deep Learning and AI to efficiently use GPU resources. In this type of use case, users read from a distributed file system, do data manipulation and filtering to get the data into a format that the Deep Learning algorithm needs for training or inference and then sends the data into a Deep Learning algorithm. Using stage level scheduling combined with accelerator aware scheduling enables users to seamlessly go from ETL to Deep Learning running on the GPU by adjusting the container requirements for different stages in Spark within the same application. This makes writing these applications easier and can help with hardware utilization and costs.
There are other ETL use cases where users want to change CPU and memory resources between stages, for instance there is data skew or perhaps the data size is much larger in certain stages of the application. In this talk, I will go over the feature details, cluster requirements, the API and use cases. I will demo how the stage level scheduling API can be used by Horovod to seamlessly go from data preparation to training using the Tensorflow Keras API using GPUs.
The talk will also touch on other new Apache Spark 3.1 functionality, such as pluggable caching, which can be used to enable faster dataframe access when operating from GPUs.
Simplify Data Conversion from Spark to TensorFlow and PyTorch
In this talk, I would like to introduce an open-source tool built by our team that simplifies the data conversion from Apache Spark to deep learning frameworks.
Imagine you have a large dataset, say 20 GBs, and you want to use it to train a TensorFlow model. Before feeding the data to the model, you need to clean and preprocess your data using Spark. Now you have your dataset in a Spark DataFrame. When it comes to the training part, you may have the problem: How can I convert my Spark DataFrame to some format recognized by my TensorFlow model?
The existing data conversion process can be tedious. For example, to convert an Apache Spark DataFrame to a TensorFlow Dataset file format, you need to either save the Apache Spark DataFrame on a distributed filesystem in parquet format and load the converted data with third-party tools such as Petastorm, or save it directly in TFRecord files with spark-tensorflow-connector and load it back using TFRecordDataset. Both approaches take more than 20 lines of code to manage the intermediate data files, rely on different parsing syntax, and require extra attention for handling vector columns in the Spark DataFrames. In short, all these engineering frictions greatly reduced the data scientists’ productivity.
The Databricks Machine Learning team contributed a new Spark Dataset Converter API to Petastorm to simplify these tedious data conversion process steps. With the new API, it takes a few lines of code to convert a Spark DataFrame to a TensorFlow Dataset or a PyTorch DataLoader with default parameters.
In the talk, I will use an example to show how to use the Spark Dataset Converter to train a Tensorflow model and how simple it is to go from single-node training to distributed training on Databricks.
Scaling your Data Pipelines with Apache Spark on Kubernetes
There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal.
In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Pipelines have become ubiquitous, as the need for stringing multiple functions to compose applications has gained adoption and popularity. Common pipeline abstractions such as “fit” and “transform” are even shared across divergent platforms such as Python Scikit-Learn and Apache Spark.
Scaling pipelines at the level of simple functions is desirable for many AI applications, however is not directly supported by Ray’s parallelism primitives. In this talk, Raghu will describe a pipeline abstraction that takes advantage of Ray’s compute model to efficiently scale arbitrarily complex pipeline workflows. He will demonstrate how this abstraction cleanly unifies pipeline workflows across multiple platforms such as Scikit-Learn and Spark, and achieves nearly optimal scale-out parallelism on pipelined computations.
Attendees will learn how pipelined workflows can be mapped to Ray’s compute model and how they can both unify and accelerate their pipelines with Ray.
Sawtooth Windows for Feature Aggregations
In this talk about zipline, we will introduce a new type of windowing construct called a sawtooth window. We will describe various properties about sawtooth windows that we utilize to achieve online-offline consistency, while still maintaining high-throughput, low-read latency and tunable write latency for serving machine learning features.We will also talk about a simple deployment strategy for correcting feature drift – due operations that are not “abelian groups”, that operate over change data.
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark.
Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue
· Why?
o Custom queries on top a table; We load the data once and query N times
· Why not Structured Streaming
· Working Solution using Redis
Niche 2 : Distributed Counters
· Problems with Spark Accumulators
· Utilize Redis Hashes as distributed counters
· Precautions for retries and speculative execution
· Pipelining to improve performance
Re-imagine Data Monitoring with whylogs and Spark
In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data.
In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.
Raven: End-to-end Optimization of ML Prediction Queries
Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components.
We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure.
This allows us to introduce optimization rules that
(i) reduce unnecessary computations by passing information between the data processing and ML operators
(ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and
(iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator.
We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.
Processing Large Datasets for ADAS Applications using Apache Spark
Semantic segmentation is the classification of every pixel in an image/video. The segmentation partitions a digital image into multiple objects to simplify/change the representation of the image into something that is more meaningful and easier to analyze [1][2]. The technique has a wide variety of applications ranging from perception in autonomous driving scenarios to cancer cell segmentation for medical diagnosis.
Exponential growth in the datasets that require such segmentation is driven by improvements in the accuracy and quality of the sensors generating the data extending to 3D point cloud data. This growth is further compounded by exponential advances in cloud technologies enabling the storage and compute available for such applications. The need for semantically segmented datasets is a key requirement to improve the accuracy of inference engines that are built upon them.
Streamlining the accuracy and efficiency of these systems directly affects the value of the business outcome for organizations that are developing such functionalities as a part of their AI strategy.
This presentation details workflows for labeling, preprocessing, modeling, and evaluating performance/accuracy. Scientists and engineers leverage domain-specific features/tools that support the entire workflow from labeling the ground truth, handling data from a wide variety of sources/formats, developing models and finally deploying these models. Users can scale their deployments optimally on GPU-based cloud infrastructure to build accelerated training and inference pipelines while working with big datasets. These environments are optimized for engineers to develop such functionality with ease and then scale against large datasets with Spark-based clusters on the cloud.
Massive Data Processing in Adobe Using Delta Lake
At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences.
What are we storing?
Multi Source – Multi Channel Problem
Data Representation and Nested Schema Evolution
Performance Trade Offs with Various formats
Go over anti-patterns used
(String FTW)
Data Manipulation using UDFs
Writer Worries and How to Wipe them Away
Staging Tables FTW
Datalake Replication Lag Tracking
Performance Time!
More from Databricks (20)
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
Democratizing Data Quality Through a Centralized Platform
Democratizing Data Quality Through a Centralized Platform
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
Stage Level Scheduling Improving Big Data and AI Integration
Stage Level Scheduling Improving Big Data and AI Integration
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Scaling your Data Pipelines with Apache Spark on Kubernetes
Scaling your Data Pipelines with Apache Spark on Kubernetes
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Raven: End-to-end Optimization of ML Prediction Queries
Raven: End-to-end Optimization of ML Prediction Queries
Processing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache Spark
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一比一原版(UofS毕业证书)萨省大学毕业证如何办理
原版定制【微信:41543339】【(UofS毕业证书)萨省大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）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。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【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【微信41543339】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41543339】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
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6:个人职称评审加20分
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留信网服务项目：
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2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
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我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdf
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一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
原版定制【微信:41543339】【(Bradford毕业证书)布拉德福德大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）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。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【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【微信41543339】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41543339】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
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【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。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(Dalhousie毕业证书)达尔豪斯大学毕业证如何办理
原版定制【微信:41543339】【(Dalhousie毕业证书)达尔豪斯大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）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。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【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【微信41543339】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41543339】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
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【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。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness in Kansas City, MO
To: Tom Tresser
From: Roger Warren
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Adjusting primitives for graph : SHORT REPORT / NOTES
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
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
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Malana- Gimlet Market Analysis (Portfolio 2)
A market analysys on Spotify's parent Podcast company Gimlet.
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
原版一模一样【微信：741003700 】【(uts毕业证书)悉尼科技大学毕业证学历证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。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，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
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、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
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
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
毕业原版【微信:41543339】【(爱大毕业证书)爱丁堡大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）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。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【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【微信41543339】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41543339】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
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【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。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(UniSA毕业证书)南澳大学毕业证如何办理
原版定制【微信:41543339】【(UniSA毕业证书)南澳大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）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。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【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【微信41543339】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41543339】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
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【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。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Adjusting OpenMP PageRank : SHORT REPORT / NOTES
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Analysis insight about a Flyball dog competition team's performance
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
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
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4:这个认证书并且可以归档倒地方
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我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
Object oriented system analysis and design
一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理
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（详情请加一下 文凭顾问+微信:95270640）欢迎咨询！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须绕过高楼旁边的垃圾堆下八个台阶才到父亲的家很狭小除了一张单人床和一张小方桌几乎没有多余的空间山娃一下子就联想起学校的男小便处山娃很想笑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间
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Apache Spark-Based Stratification Library for Machine Learning Use Cases at Netflix with Shiva Chaitanya
- 1. Shiva Chaitanya, Netflix #DSSAIS11 Stratification Library For Machine Learning Use Cases At Netflix
- 2. • Iterative ML in Netflix • Boson Library • Data Sampling (Stratification) Needs • API Examples Outline
- 3. Title Ranking Everything is a Recommendation RowSelection&Ordering Recommendations are driven by machine learning algorithms Over 80% of what members watch comes from our recommendations Image
- 4. • Try an idea offline using historical data to see if it would have made better recommendations • If it would, deploy a live A/B test to see if it performs well in production Running Experiments
- 5. • When experimenting offline, iterate quickly • When ready to move to production, transition must be smooth • Spark + Scala! ML Research Pipeline Hypothesize Prototype Analyze A/B Test Productize
- 6. Rapid Iterations • Developing machine learning is iterative • Need a shortened pipeline to rapidly try ideas • Entire pipeline must be amenable to correct experimentation • Down-sampled data should satisfy desired distribution for model training/testing
- 7. • A high level Spark/Scala API for ML exploration • Focuses on Offline Feature Engineering/Training for both • Ad-hoc exploration • Production • Think “Subset of SKLearn” for Scala/JVM ecosystem • Spark’s Dataframe is the core data abstraction Boson Overview
- 8. Design Experiment Collect Label Dataset Model Training Compute Validation Metrics Model Testing Design a New Experiment to Test Out Different Ideas Offline ML Experiment Online System Online AB Testing Running Experiments Offline Feature Generation Snapshots Infrastructure Stratify User Cohorts Capture Facts Stratify Training Set Runs once a day
- 9. Design Experiment Collect Label Dataset Model Training Compute Validation Metrics Model Testing Design a New Experiment to Test Out Different Ideas Offline ML Experiment Online System Online AB Testing Offline Feature Generation Snapshots Infrastructure Stratify User Cohorts Capture Facts Stratify Training Set Runs once a day Stratify User Cohorts Stratify Training Set Running Experiments
- 10. Data Stratification • A mechanism to down-sample datasets while meeting a desired distribution • Place constraints on snapshotted users while ensuring maximal training data yield • Place constraints on training data to make sure a model learns enough about important/newer demographics • Several criteria: Small Countries, Emerging Markets, New Members ...
- 11. Flexible Dataframe Stratification API • Extended stratification methods on Spark dataframes • Think sampling rules specified with native spark SQL-like expressions dataframe.stratify(rules = Seq( $(“country”) == ‘US’ maxPercent 8.0, $(“tenure”) > 10 && $(“plays”) > 1 minPercent 0.5, … ))
- 12. Sampling Netflix Userbase • Spark + Scala DSL with strong type safety • Expressive power to build arbitrary user-sets on demand • Declarative API that informs the library what to do, not how • Large set of supported user attributes to stratify by – E.g Country, Tenure, Plays, Searches, etc
- 13. User stratification client User Stratification Client De-normalized Data Snapshots Specify sampling rules
- 14. Building Blocks • Country.US represents US users. US US && M1 M1 || Plays(1, 10) !US && Tenure(10, 100) • Country.US && Tenure.M1 comprises of US users AND also in first membership month. • Tenure.M1 || Plays(1, 10)) comprises of users in their first membership month OR having 1 <= number-of-plays < 10 • !Country.US && Tenure(10, 100)) comprises of international user profiles AND with tenure in the range 10 - 100 days.
- 15. API Example: Specify rules on counts new UserSet( samplingRules = Map( Country.US -> TargetCount(500), Country.US && Tenure.M1 -> TargetCount(100), Country.US && Tenure.M2 -> MinCount(200), Country(“GB”) && Plays(1) -> MaxCount(200), … … ) )
- 16. API Example: Specify rules on percentages new UserSet( samplingRules = Map( Country.US -> TargetPercent(40.0), Country.US && Tenure.M1 -> TargetPercent(20.0), Country.US && Tenure.M2 -> MinPercent(10.0), Country(“GB”) && Plays(1) -> MaxPercent(5.0), … … ) )
- 17. API Example: EACH Expander new UserSet( samplingRules = Map( Tenure.M1 -> TargetPercent(10.0), Country.EACH && Tenure.M2 -> MaxPercent(0.2), … ) ) Country(“AD”) && Tenure.M2 -> MaxPercent(0.2), Country(“AE”) && Tenure.M2 -> MaxPercent(0.2), ….
- 18. API Example: Error margin to increase yield new UserSet( samplingRules = Map( Tenure.M1 -> TargetPercent(10.0), Country.EACH && Tenure.M2 -> MaxPercent(0.2), … … ), allowedErrorMargin = 20.0, )
- 19. Algorithm Complexity • When there are no overlapping regions in the venn diagram space • Closed expressions to determine the sampling ratio per region are possible • When the regions overlap, we formulate the optimization problem as a Linear Programming problem. • The sampling rules form the constraints • Objective is to typically maximize the sample size
- 20. LP Formulation 0 <= s1 <= C1 0 <= s2 <= C2 0 <= s12 <= C12 p1 * (C1 + C12) <= (s1 + s12) * 100 p2 * (C2 + C12) >= (s2 + s12) * 100 Maximize (s1 + s2 + s12) C2 C1 C12 minPercent p1 maxPercent p2
- 21. Optimization problem • Shout out to the vagmcs/optimus third party idiomatic scala library! https://github.com/vagmcs/Optimus
- 22. Code snippet targetDistroMap.foreach { case (samplingExpr, freq @ TargetPercent(percent)) => val relevantRegions: Seq[Region] = existingDistroRegionMap.keys.filter(_.contains(samplingExpr)).toSeq val lpExpression = relevantRegions.map(regionVariables).foldLeft[Expression](Zero)(_ + _) val leftErrorMargin = MPFloatVar(samplingExpr.toString + "_leftErrorMargin", 0, existingDistro.totalCount) val rightErrorMargin = MPFloatVar(samplingExpr.toString + "_rightErrorMargin", 0, existingDistro.totalCount) val toleranceMargin = freq.toleranceMargin.getOrElse(targetToleranceMargin) add(leftErrorMargin <:= overallSizeVariable * Const(0.01 * percent * toleranceMargin/100.0)) add(rightErrorMargin <:= overallSizeVariable * Const(0.01 * percent * toleranceMargin/100.0)) add(lpExpression := overallSizeVariable * Const(0.01 * percent) - leftErrorMargin + rightErrorMargin) leftErrorMarginsSum = leftErrorMarginsSum ++ Seq(leftErrorMargin) rightErrorMarginsSum = rightErrorMarginsSum ++ Seq(rightErrorMargin) }
- 23. Questions?