The exponential growth in data over the last decade coupled with a drastic drop in cost of storage has enabled organizations to amass a large amount of data. This vast data becomes the new natural resource that these organizations must tap in to innovate and stay ahead of the competition, and they must do so in a secure environment that protects the data throughout its lifecycle and data access in real time at any time.

When it comes to security, nothing can rival IBM® Z, the multi-workload transactional platform that powers the core business processes of the majority of the Fortune 500 enterprises with unmatched security, availability, reliability, and scalability. With core transactions and data originating on IBM Z®, it simply makes sense for analytics to exist and run on the same platform.

For years, some businesses chose to move their sensitive data off IBM Z to platforms that include data lakes, Hadoop, and warehouses for analytics processing. However, the massive growth of digital data, the punishing cost of security exposures as well as the unprecedented demand for instant actionable intelligence from data in real time have convinced them to rethink that decision and, instead, embrace the strategy of data gravity for analytics. At the core of data gravity is the conviction that analytics must exist and run where the data resides. An IBM client eloquently compares this change in analytics strategy to a shift from “moving the ocean to the boat to moving the boat to the ocean,” where the boat is the analytics and the ocean is the data.

IBM respects and invests heavily on data gravity because it recognizes the tremendous benefits that data gravity can deliver to you, including reduced cost and minimized security risks. IBM Machine Learning for z/OS® is one of the offerings that decidedly move analytics to Z where your mission-critical data resides. In the inherently secure Z environment, your machine learning scoring services can co-exist with your transactional applications and data, supporting high throughput and minimizing response time while delivering consistent service level agreements (SLAs).

This book introduces Machine Learning for z/OS version 1.1.0 and describes its unique value proposition. It provides step-by-step guidance for you to get started with the program, including best practices for capacity planning, installation and configuration, administration and operation. Through a retail example, the book shows how you can use the versatile and intuitive web user interface to quickly train, build, evaluate, and deploy a model. Most importantly, it examines use cases across industries to illustrate how you can easily turn your massive data into valuable insights with Machine Learning for z/OS.

This book was written by a team of experts across geographies working on the IBM Machine Learning for z/OS project.

Samantha Buhler serves as an Offering Leader for the IBM analytics software portfolio on System Z. She has over 15 years of experience with software that runs on the IBM Z platform across many functions, including pricing and product management. She holds a Master of Business Administration degree from The College of William and Mary. Her areas of expertise include industry and market analysis, pricing, and go-to-market strategy for new offerings.

Guanjun Cai is an Information Architect for Machine Learning for z/OS and IBM Db2® for z/OS at IBM in the United States. He has 20 years of experience in designing and developing content for IBM analytics, enterprise storage, and z/OS products. He holds a Ph.D. in Rhetoric, Composition, and the Teaching of English from the University of Arizona. His areas of expertise include information architecture, development, and publishing as well as user experience design, markup languages, and automation.

John Goodyear is a Client Technical Specialist at the IBM Washington System Center in the United States. He has 25 years of experience in the computer industry. He holds a BA in Computer Science from University of Maryland. His areas of expertise include Machine Learning and Spark on z/OS, C, C++, Java, and Perl programming, Linux, IBM AIX®, and moderate z/OS administration.

Edrian Irizarry is a z/OS DevOps Architect in the United States. He has 3.5 years of experience in the field of software engineering. He holds a degree in Computer Science from the University of Rochester. His areas of expertise include Package Management, DevOps, and Automation.

Nora Kissari is a Data Science Specialist on the Z Advanced Technical Specialist Team for Europe, Middle-East, and Africa. She has 4 years of experience helping customers start new analytics projects. She holds a degree in Computer Science - Software Architecture and Engineering from the University of Montpellier in France. Her areas of expertise include designing data-centric architecture as well as hands-on data management, data processing, business intelligence and data modeling skills. She has lectured extensively on these topics at international events around the world.

Zhuo Ling is a senior data scientist and an IBM-certified information architect at the IBM China Development Lab and a lead data scientist on the MLz development team. She has over 20 years of experience in the field of data science. She holds a master degree in computer science from Peking University in China. Her areas of expertise include data analytics, data modeling and business intelligence solution architectural design. She has rich domain knowledge on banking, transportation and renewable energy, as well as IT operational analytics on z/OS.

Nicholas Marion is a Staff Software Engineer and Service Team Lead for Open Data Analytics in Poughkeepsie, NY. He has over 3 years of experience in the field of software design, development, and test. He holds a degree in Computer Science from the State University of New York at New Paltz. His areas of expertise include operating system development, development and test automation, and product support. He has previously had publications on IBM developerWorks®.

Aleksandr Petrov is a manager of the Data Science Elite (US) team, IBM Analytics, and specializes in Analytics platforms, Big Data, and Data Mining solutions. Over the years, he has delivered Analytics products that contain the implementation of ETL engine, machine learning, NLP, data visualization, and distributed algorithms implementation. Alexander has more than 20 years of industry experience working as a software architect, data scientist, and manager on creating software products and developing new technologies.

Junfei Shen is a data scientist in China. She has 1.5 years of experience in the field of data science. She holds a Master of Arts degree from Columbia University. Her areas of expertise include data science, machine learning, statistics, and finance.

Wanting Wang is a Data Scientist on the Data Science Elite Team in the United States. She has 2 years of experience working with clients to analyze data and derive business insights. She holds a degree in Quantitative Methods in the Social Sciences from Columbia University, with an interdisciplinary background in social science, statistics, and computer science. Her areas of expertise include statistics, machine learning, and data visualization.

He Sheng Yang is a Data Scientist and Enablement of IBM Analytics in China. He has 15 years of experience in the field of software engineering. He holds a degree in Computer Science from Beijing Institute of Technology. His areas of expertise include data science, solution architect, software enablement, project management in data analytics, healthcare industry, enterprise content management. He has written extensively for developerWorks and software engineering books.

Dai Yi is a Data Scientist in China. He has more than 5 years of experience in software development and data analysis. He holds a doctorate degree in Statistics and a master degree in Computer Science from the Renmin University of China. His areas of expertise include Software Engineer, Machine Learning, Statistics Learning, and Big Data.

Xavier Yuen is an Advisory Engineer in USA. He has 20 years of experience in Software Test. He holds a degree in Computer Science from USC, Sacramento.

Hao Zhang is a Machine Learning for z/OS Architect in China. He has 15 years of experience in the field of software engineering. He holds a master degree in Computer Science from the Chinese Academy of Sciences. His areas of expertise include Kubernetes installation and deployment. He has written extensively on installing and troubleshooting IBM Machine Learning for z/OS.

Thanks to the following people for their contributions to the book:

Special thanks to Jane Dong for coordinating teams across geographies to ensure timely delivery of the book.

Thanks to Guanjun Cai for providing information architecture and design guidance for the book.

This book was produced by the IBM Redbooks® publication team with valuable contributions from Martin Keen, IBM Redbooks Project Leader.

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