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
