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