Create scalable machine learning applications to power a modern data-driven business using Spark 2.x
About This Book
- Get to the grips with the latest version of Apache Spark
- Utilize Spark's machine learning library to implement predictive analytics
- Leverage Spark's powerful tools to load, analyze, clean, and transform your data
Who This Book Is For
If you have a basic knowledge of machine learning and want to implement various machine-learning concepts in the context of Spark ML, this book is for you. You should be well versed with the Scala and Python languages.
What You Will Learn
- Get hands-on with the latest version of Spark ML
- Create your first Spark program with Scala and Python
- Set up and configure a development environment for Spark on your own computer, as well as on Amazon EC2
- Access public machine learning datasets and use Spark to load, process, clean, and transform data
- Use Spark's machine learning library to implement programs by utilizing well-known machine learning models
- Deal with large-scale text data, including feature extraction and using text data as input to your machine learning models
- Write Spark functions to evaluate the performance of your machine learning models
In Detail
This book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML.
Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML.
By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business.
Style and approach
This practical tutorial with real-world use cases enables you to develop your own machine learning systems with Spark. The examples will help you combine various techniques and models into an intelligent machine learning system.
Rajdeep Dua has over 16 years of experience in the Cloud and Big Data space. He worked in the advocacy team for Google's big data tools, BigQuery. He worked on the Greenplum big data platform at VMware in the developer evangelist team. He also worked closely with a team on porting Spark to run on VMware's public and private cloud as a feature set. He has taught Spark and Big Data at some of the most prestigious tech schools in India: IIIT Hyderabad, ISB, IIIT Delhi, and College of Engineering Pune.
Currently, he leads the developer relations team at Salesforce India. He also works with the data pipeline team at Salesforce, which uses Hadoop and Spark to expose big data processing tools for developers.
His contributions to the open source community are related to Docker, Kubernetes, Android, OpenStack, and cloudfoundry.
Manpreet Singh Ghotra has more than 12 years of experience in software development for both enterprise and big data software. He is currently working on developing a machine learning platform using Apache Spark at Salesforce. He has worked on a sentiment analyzer using the Apache stack and machine learning.
He was part of the machine learning group at one of the largest online retailers in the world, working on transit time calculations using Apache Mahout and the R Recommendation system using Apache Mahout.
With a master's and postgraduate degree in machine learning, he has contributed to and worked for the machine learning community.
Nick Pentreath has a background in financial markets, machine learning, and software development. He has worked at Goldman Sachs Group, Inc., as a research scientist at the online ad targeting start-up, Cognitive Match Limited, London, and led the data science and analytics team at Mxit, Africa's largest social network.
He is a cofounder of Graphflow, a big data and machine learning company focused on user-centric recommendations and customer intelligence. He is passionate about combining commercial focus with machine learning and cutting-edge technology to build intelligent systems that learn from data to add value to the bottom line.
Nick is a member of the Apache Spark Project Management Committee.
