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polak scaling machine learning spark designing pytorch 2023

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Textbook in PDF format 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. This book aims to guide you in your journey as you learn more about Machine Learning (ML) systems. Apache Spark is currently the most popular framework for large-scale data processing. It has numerous APIs implemented in Python, Java, and Scala and is used by many powerhouse companies, including Netflix, Microsoft, and Apple. PyTorch and TensorFlow are among the most popular frameworks for machine learning. Combining these tools, which are already in use in many organizations today, allows you to take full advantage of their strengths. 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 Who Should Read This Book? This book is designed for Machine Learning practitioners with previous industry experience who want to learn about Apache Spark’s MLlib and increase their understanding of the overall system and flow. It will be particularly relevant to data scientists and machine learning engineers, but MLOps engineers, software engineers, and anyone interested in learning about or building distributed machine learning models and building pipelines with MLlib, distributed PyTorch, and TensorFlow will also find value. Technologists who understand high-level concepts of working with Machine Learning and want to dip their feet into the technical side as well should also find the book interesting and accessible

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Polak A. Scaling Machine Learning with Spark. Designing...with PyTorch 2023.pdf 7.3 MB

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polak scaling machine learning spark designing pytorch 2023