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Scaling Machine Learning with Spark Distributed ML with MLlib, TensorFlow, and PyTorch
Free Download Scaling Machine Learning with Spark: Distributed ML with MLlib, TensorFlow, and PyTorch by Adi PolakEnglish | April 11th, 2023 | ISBN: 1098106822 | 291 pages | True EPUB | 6.93 MB
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
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