Book Image

Mastering Machine Learning with Spark 2.x

By : Michal Malohlava, Alex Tellez, Max Pumperla
Book Image

Mastering Machine Learning with Spark 2.x

By: Michal Malohlava, Alex Tellez, Max Pumperla

Overview of this book

The purpose of machine learning is to build systems that learn from data. Being able to understand trends and patterns in complex data is critical to success; it is one of the key strategies to unlock growth in the challenging contemporary marketplace today. With the meteoric rise of machine learning, developers are now keen on finding out how can they make their Spark applications smarter. This book gives you access to transform data into actionable knowledge. The book commences by defining machine learning primitives by the MLlib and H2O libraries. You will learn how to use Binary classification to detect the Higgs Boson particle in the huge amount of data produced by CERN particle collider and classify daily health activities using ensemble Methods for Multi-Class Classification. Next, you will solve a typical regression problem involving flight delay predictions and write sophisticated Spark pipelines. You will analyze Twitter data with help of the doc2vec algorithm and K-means clustering. Finally, you will build different pattern mining models using MLlib, perform complex manipulation of DataFrames using Spark and Spark SQL, and deploy your app in a Spark streaming environment.
Table of Contents (9 chapters)
Ensemble Methods for Multi-Class Classification


This chapter summarizes everything you learned throughout the book with end-to-end examples. We analyzed the data, transformed it, performed several experiments to figure out how to set up the model-training pipeline, and built models. The chapter also stresses on the need for well-designed code, which can be shared across several projects. In our example, we created a shared library that was used at the time of training as well as being utilized during the scoring time. This was demonstrated on the critical operation called "model deployment" when trained models and related artifacts are used to score unseen data.

This chapter also brings us to the end of the book. Our goal was to show that solving machine learning challenges with Spark is mainly about experimentation with data, parameters, models, debugging data / model-related issues, writing code that can...