Adi Polak – författare

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 terminologyManage the ML lifecycle with MLflowIngest data and perform basic preprocessing with SparkExplore feature engineering, and use Spark to extract featuresTrain a model with MLlib and build a pipeline to reproduce itBuild a data system to combine the power of Spark with deep learningGet a step-by-step example of working with distributed TensorFlowUse PyTorch to scale machine learning and its internal architecture

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 terminologyManage the ML lifecycle with MLflowIngest data and perform basic preprocessing with SparkExplore feature engineering, and use Spark to extract featuresTrain a model with MLlib and build a pipeline to reproduce itBuild a data system to combine the power of Spark with deep learningGet a step-by-step example of working with distributed TensorFlowUse PyTorch to scale machine learning and its internal architecture

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 terminologyManage the ML lifecycle with MLflowIngest data and perform basic preprocessing with SparkExplore feature engineering, and use Spark to extract featuresTrain a model with MLlib and build a pipeline to reproduce itBuild a data system to combine the power of Spark with deep learningGet a step-by-step example of working with distributed TensorFlowUse PyTorch to scale machine learning and its internal architecture

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, Adi Polak, and Rachel Warren 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 4.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 PyTorchHandle key skew and take advantage of Spark's new dynamic partitioningMake your code reliable with scalable testing and validation techniquesMake Spark high performanceDeploy Spark on Kubernetes and similar environmentsTake advantage of GPU acceleration with RAPIDS and resource profilesGet your Spark jobs to run fasterUse Spark to productionize exploratory data science projectsHandle even larger datasets with SparkGain faster insights by reducing pipeline running times

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, Adi Polak, and Rachel Warren 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 4.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 PyTorchHandle key skew and take advantage of Spark's new dynamic partitioningMake your code reliable with scalable testing and validation techniquesMake Spark high performanceDeploy Spark on Kubernetes and similar environmentsTake advantage of GPU acceleration with RAPIDS and resource profilesGet your Spark jobs to run fasterUse Spark to productionize exploratory data science projectsHandle even larger datasets with SparkGain faster insights by reducing pipeline running times