Mastering Machine Learning with Spark 2.x

Discover how to implement scalable machine learning systems using Apache Spark in "Mastering Machine Learning with Spark 2.x." This comprehensive guide starts with foundational concepts and leads you step-by-step through building sophisticated machine learning solutions using Spark's MLlib and H2O libraries. Equip yourself with the tools to extract actionable insights from large, complex datasets.

What this Book will help me do

• Learn to build Spark pipelines for efficient data processing.
• Use MLlib to create scalable machine learning models.
• Master techniques such as classification, regression, and clustering.
• Understand Spark SQL for handling complex DataFrame manipulations.
• Deploy machine learning models in streaming environments using Spark.

Author(s)

None Malohlava, None Tellez, and Max Pumperla are experienced contributors to the fields of machine learning and big data analytics. With professional backgrounds working on distributed computation and teaching technical subjects, they provide clear insights into complex topics. Their goal is to offer practical and actionable knowledge about using Spark to solve real-world problems.

Who is it for?

This book is designed for software developers and data scientists who are familiar with machine learning concepts and wish to scale their skills to handle big data using Spark. Readers should have a working knowledge of programming and basic experience with Spark. If you're looking to harness scalable machine learning techniques for real-world challenges, this book is crafted for you.