Scaling Machine Learning with Spark: Distributed ML with MLlib, TensorFlow, and PyTorch

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