Published on September 11, 2018
IBM Form #: SG24-8421-00
Authors: Samantha Buhler, Guanjun Cai, John Goodyear, Edrian Irizarry, Nora Kissari, Zhuo Ling, Nicholas Marion, Aleksandr Petrov, Junfei Shen, Wanting Wang, He Sheng Yang, Dai Yi, Xavier Yuen and Hao Zhang
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 lifecyle 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.
Chapter 1. Overview
Chapter 2. Planning
Chapter 3. Installation and customization
Chapter 4. Administration and operation
Chapter 5. Model development and deployment: A retail example
Chapter 6. Use cases: Applying Machine Learning for z/OS in business
Appendix A. Machine learning basics
Appendix B. R and RStudio