Synopsis
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
"synopsis" may belong to another edition of this title.
About the Author
Adi Polak is an open source technologist who believes in communities and education, and their ability to positively impact the world around us. She is passionate about building a better world through open collaboration and technological innovation. As a seasoned engineer and Vice President of Developer Experience at Treeverse, Adi shapes the future of data and ML technologies for hands-on builders. She serves on multiple program committees and acts as an advisor for conferences like Data & AI Summit by Databricks, Current by Confluent, and Scale by the Bay, among others. Adi previously served as a senior manager for Azure at Microsoft, where she helped build advanced analytics systems and modern data architectures. Adi gained experience in machine learning by conducting research for IBM, Deutsche Telekom, and other Fortune 500 companies.
"About this title" may belong to another edition of this title.
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Paperback. Condition: New. 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. Seller Inventory # LU-9781098106829
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