Scaling Machine Learning With Spark
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Scaling Machine Learning With Spark
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Author : Adi Polak
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-03-07
Scaling Machine Learning With Spark written by Adi Polak and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-07 with Computers categories.
Learn how to build end-to-end scalable machine learning solutions with Apache Spark. With this practical guide, author Adi Polak introduces data and ML practitioners to creative solutions that supersede today's traditional methods. You'll learn a more holistic approach that takes you beyond specific requirements and organizational goals--allowing data and ML practitioners to collaborate and understand each other better. Scaling Machine Learning with Spark examines several technologies for building end-to-end distributed ML workflows based on the Apache Spark ecosystem with Spark MLlib, MLflow, TensorFlow, and PyTorch. If you're a data scientist who works with machine learning, this book shows you when and why to use each technology. You will: Explore machine learning, including distributed computing concepts and terminology Manage the ML lifecycle with MLflow Ingest data and perform basic preprocessing with Spark Explore feature engineering, and use Spark to extract features Train a model with MLlib and build a pipeline to reproduce it Build a data system to combine the power of Spark with deep learning Get a step-by-step example of working with distributed TensorFlow Use PyTorch to scale machine learning and its internal architecture
Scaling Machine Learning With Spark
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Author : Adi Polak
language : en
Publisher: O'Reilly Media
Release Date : 2023-04-04
Scaling Machine Learning With Spark written by Adi Polak and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-04 with categories.
Get up to speed on Apache Spark, the popular engine for large-scale data processing, including machine learning and analytics. If you're looking to expand your skill set or advance your career in scalable machine learning with MLlib, distributed PyTorch, and distributed TensorFlow, this practical guide is for you. Using Spark as your main data processing platform, you'll discover several open source technologies designed and built for enriching Spark's ML capabilities. Scaling Machine Learning with Spark examines various technologies for building end-to-end distributed ML workflows based on the Apache Spark ecosystem with Spark MLlib, MLFlow, TensorFlow, PyTorch, and Petastorm. This book shows you when to use each technology and why. If you're a data scientist working with machine learning, you'll learn how to: Build practical distributed machine learning workflows, including feature engineering and data formats Extend deep learning functionalities beyond Spark by bridging into distributed TensorFlow and PyTorch Manage your machine learning experiment lifecycle with MLFlow Use Petastorm as a storage layer for bridging data from Spark into TensorFlow and PyTorch Use machine learning terminology to understand distribution strategies
High Performance Spark
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Author : Holden Karau
language : en
Publisher: O'Reilly Media
Release Date : 2026-01-31
High Performance Spark written by Holden Karau and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-01-31 with Computers categories.
Apache Spark is amazing when everything clicks. But if you haven't seen the performance improvements you expected or still don't feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau, Rachel Warren, and Anya Bida walk you through the secrets of the Spark code base, and demonstrate performance optimizations that will help your data pipelines run faster, scale to larger datasets, and avoid costly antipatterns. Ideal for data engineers, software engineers, data scientists, and system administrators, the second edition of High Performance Spark presents new use cases, code examples, and best practices for Spark 3.x and beyond. This book gives you a fresh perspective on this continually evolving framework and shows you how to work around bumps on your Spark and PySpark journey. With this book, you'll learn how to: Accelerate your ML workflows with integrations including PyTorch Handle key skew and take advantage of Spark's new dynamic partitioning Make your code reliable with scalable testing and validation techniques Make Spark high performance Deploy Spark on Kubernetes and similar environments Take advantage of GPU acceleration with RAPIDS and resource profiles Get your Spark jobs to run faster Use Spark to productionize exploratory data science projects Handle even larger datasets with Spark Gain faster insights by reducing pipeline running times
High Performance Spark
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Author : Holden Karau
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-05-25
High Performance Spark written by Holden Karau and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-25 with Computers categories.
Apache Spark is amazing when everything clicks. But if you haven’t seen the performance improvements you expected, or still don’t feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries run faster and handle larger data sizes, while using fewer resources. Ideal for software engineers, data engineers, developers, and system administrators working with large-scale data applications, this book describes techniques that can reduce data infrastructure costs and developer hours. Not only will you gain a more comprehensive understanding of Spark, you’ll also learn how to make it sing. With this book, you’ll explore: How Spark SQL’s new interfaces improve performance over SQL’s RDD data structure The choice between data joins in Core Spark and Spark SQL Techniques for getting the most out of standard RDD transformations How to work around performance issues in Spark’s key/value pair paradigm Writing high-performance Spark code without Scala or the JVM How to test for functionality and performance when applying suggested improvements Using Spark MLlib and Spark ML machine learning libraries Spark’s Streaming components and external community packages
Scala And Spark For Big Data Analytics
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Author : Md. Rezaul Karim
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-07-25
Scala And Spark For Big Data Analytics written by Md. Rezaul Karim and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-25 with Computers categories.
Harness the power of Scala to program Spark and analyze tonnes of data in the blink of an eye! About This Book Learn Scala's sophisticated type system that combines Functional Programming and object-oriented concepts Work on a wide array of applications, from simple batch jobs to stream processing and machine learning Explore the most common as well as some complex use-cases to perform large-scale data analysis with Spark Who This Book Is For Anyone who wishes to learn how to perform data analysis by harnessing the power of Spark will find this book extremely useful. No knowledge of Spark or Scala is assumed, although prior programming experience (especially with other JVM languages) will be useful to pick up concepts quicker. What You Will Learn Understand object-oriented & functional programming concepts of Scala In-depth understanding of Scala collection APIs Work with RDD and DataFrame to learn Spark's core abstractions Analysing structured and unstructured data using SparkSQL and GraphX Scalable and fault-tolerant streaming application development using Spark structured streaming Learn machine-learning best practices for classification, regression, dimensionality reduction, and recommendation system to build predictive models with widely used algorithms in Spark MLlib & ML Build clustering models to cluster a vast amount of data Understand tuning, debugging, and monitoring Spark applications Deploy Spark applications on real clusters in Standalone, Mesos, and YARN In Detail Scala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you. The first part introduces you to Scala, helping you understand the object-oriented and functional programming concepts needed for Spark application development. It then moves on to Spark to cover the basic abstractions using RDD and DataFrame. This will help you develop scalable and fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. Finally, the book moves on to some advanced topics, such as monitoring, configuration, debugging, testing, and deployment. You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics using Zeppelin, and in-memory data processing with Alluxio. By the end of this book, you will have a thorough understanding of Spark, and you will be able to perform full-stack data analytics with a feel that no amount of data is too big. Style and approach Filled with practical examples and use cases, this book will hot only help you get up and running with Spark, but will also take you farther down the road to becoming a data scientist.
Learning Spark
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Author : Jules S. Damji
language : en
Publisher: O'Reilly Media
Release Date : 2020-07-16
Learning Spark written by Jules S. Damji and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-16 with Computers categories.
Data is bigger, arrives faster, and comes in a variety of formatsâ??and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark. Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, youâ??ll be able to: Learn Python, SQL, Scala, or Java high-level Structured APIs Understand Spark operations and SQL Engine Inspect, tune, and debug Spark operations with Spark configurations and Spark UI Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka Perform analytics on batch and streaming data using Structured Streaming Build reliable data pipelines with open source Delta Lake and Spark Develop machine learning pipelines with MLlib and productionize models using MLflow
Apache Spark Machine Learning Cookbook
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Author : Siamak Amirghodsi
language : en
Publisher:
Release Date : 2016-10-31
Apache Spark Machine Learning Cookbook written by Siamak Amirghodsi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-31 with categories.
Over 80 recipes to simplify machine learning model implementations with SparkAbout This Book*Solve the day-to-day problems of data science with Spark*This unique cookbook consists of exciting and intuitive numerical recipes*Optimize your work by acquiring, cleaning, analyzing, predicting, and visualizing your dataWho This Book Is ForThis book is for Scala developers with a fairly good exposure to and understanding of machine learning techniques, but lack practical implementations with Spark. A solid knowledge of machine learning algorithms is assumed, as well as hands-on experience of implementing ML algorithms with Scala. However, you do not need to be acquainted with the Spark ML libraries and ecosystem.What You Will Learn*Get to know how Scala and Spark go hand-in-hand for developers when developing ML systems with Spark*Build a recommendation engine that scales with Spark*Find out how to build unsupervised clustering systems to classify data in Spark*Build machine learning systems with the Decision Tree and Ensemble models in Spark*Deal with the curse of high-dimensionality in big data using Spark*Implement Text analytics for Search Engines in Spark*Streaming Machine Learning System implementation using SparkIn DetailMachine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to bleeding edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a cluster computing system well suited for large-scale machine learning tasks.This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered. It also highlights some key issues developers face while thinking about Scala for machine learning and during the switch over to Spark. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, clustering and learning systems. Towards the final chapters, we'll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems.
Apache Spark 2 X Machine Learning Cookbook
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Author : Siamak Amirghodsi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-09-22
Apache Spark 2 X Machine Learning Cookbook written by Siamak Amirghodsi and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-22 with Computers categories.
Simplify machine learning model implementations with Spark About This Book Solve the day-to-day problems of data science with Spark This unique cookbook consists of exciting and intuitive numerical recipes Optimize your work by acquiring, cleaning, analyzing, predicting, and visualizing your data Who This Book Is For This book is for Scala developers with a fairly good exposure to and understanding of machine learning techniques, but lack practical implementations with Spark. A solid knowledge of machine learning algorithms is assumed, as well as hands-on experience of implementing ML algorithms with Scala. However, you do not need to be acquainted with the Spark ML libraries and ecosystem. What You Will Learn Get to know how Scala and Spark go hand-in-hand for developers when developing ML systems with Spark Build a recommendation engine that scales with Spark Find out how to build unsupervised clustering systems to classify data in Spark Build machine learning systems with the Decision Tree and Ensemble models in Spark Deal with the curse of high-dimensionality in big data using Spark Implement Text analytics for Search Engines in Spark Streaming Machine Learning System implementation using Spark In Detail Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to cutting edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a resilient cluster computing system well suited for large-scale machine learning tasks. This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we'll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems. Style and approach This book is packed with intuitive recipes supported with line-by-line explanations to help you understand how to optimize your work flow and resolve problems when working with complex data modeling tasks and predictive algorithms. This is a valuable resource for data scientists and those working on large scale data projects.
Machine Learning With Spark
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Author : Nick Pentreath
language : en
Publisher:
Release Date : 2015-02-20
Machine Learning With Spark written by Nick Pentreath and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-02-20 with Machine learning categories.
If you are a Scala, Java, or Python developer with an interest in machine learning and data analysis and are eager to learn how to apply common machine learning techniques at scale using the Spark framework, this is the book for you. While it may be useful to have a basic understanding of Spark, no previous experience is required.
Large Scale Machine Learning With Spark
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Author : Rezaul Karim
language : en
Publisher:
Release Date : 2016-11-30
Large Scale Machine Learning With Spark written by Rezaul Karim and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-30 with categories.
Build robust machine learning applications with Spark at scaleAbout This Book* Get the most up-to-date book on the market that focuses on design, engineering, and scalable solutions in machine learning with Spark 2* We use Spark's machine learning library in a big data environment* You will learn to develop high-value applications at scale with ease and a personalized designWho This Book Is ForThis book caters to data science engineers and scientists working with large and complex data sets. You should be familiar with the basics of machine learning concepts, statistics, and computational mathematics. Knowledge of Scala and Java is advisable.What You Will Learn* Understand R programming language and its ecosystem of packages for data science* Decide on the correct approach before solving a problem* Obtain and clean data before processing it* Master the essential exploratory techniques for summarizing data* Examine various machine learning prediction models* Explore the H2O analytics platform in R for deep learning* Apply data mining techniques to the available datasets* Work with interactive visualization packages in R* Latch on to the right approach to build data productsIn DetailScaling out and deploying algorithms, interactions, and clustering are crucial steps in the process of optimizing any application. By maintaining and streaming data, Spark can figure out when to cache data in-memory, 100x faster than Hadoop and Mahoot. This means data streaming and analytics can run and complete jobs a lot quicker, making Spark ideal for large data-intensive applications.This book focuses on design, engineering, and scalable solutions in machine learning with Spark. You will learn how to install Spark with all new features as in the latest version Spark 2. You will also get to grips with Spark MLlib and Spark ML and its implementation for machine learning algorithms. Moving ahead, we'll explore about important concepts such as Dataframes and advanced feature engineering. After studying more about the development and deployment of an application, you will also find out about the other external libraries available for your data analysis.