Scaling Machine Learning with Spark
- Paperback: 291 pages
- Publisher: WOW! eBook (April 11, 2023)
- Language: English
- ISBN-10: 1098106822
- ISBN-13: 978-1098106829
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.
- 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
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.
Exclusive Offer! Order DIY Manual Floor Furniture Repair Kit Now. Get Lowest Price & 60 Day Return Policy. Huge Discounts Available! Offer Expires Soon.