Description
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
Author: Adi Polak
Publisher: O'Reilly Media
Published: 04/11/2023
Pages: 291
Binding Type: Paperback
Weight: 1.04lbs
Size: 9.19h x 7.00w x 0.62d
ISBN13: 9781098106829
ISBN10: 1098106822
BISAC Categories:
- Computers | Data Science | Machine Learning
- Computers | Programming | Open Source
- Computers | Artificial Intelligence | General