Turning Data Into Insight with IBM Machine Learning for Z/OS
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Front cover Turning Data into Insight with IBM Machine Learning for z/OS Samantha Buhler He Sheng Yang Guanjun Cai Dai Yi John Goodyear Xavier Yuen Edrian Irizarry Hao Zhang Nora Kissari Zhuo Ling Nicholas Marion Aleksandr Petrov Junfei Shen Wanting Wang Redbooks International Technical Support Organization Turning Data into Insight with IBM Machine Learning for z/OS August 2018 SG24-8421-00 Note: Before using this information and the product it supports, read the information in “Notices” on page vii. First Edition (August 2018) This edition applies to IBM Machine Learning for z/OS version 1.1.0. © Copyright International Business Machines Corporation 2018. All rights reserved. Note to U.S. Government Users Restricted Rights -- Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. Contents Notices . vii Trademarks . viii Preface . ix Authors. ix Acknowledgements . xi Now you can become a published author, too! . xi Comments welcome. xii Stay connected to IBM Redbooks . xii Chapter 1. Overview . 1 1.1 Challenges of exponential data growth . 2 1.2 Trends in artificial intelligence and cognitive systems. 2 1.3 IBM’s approach to artificial intelligence and cognitive systems. 3 1.4 IBM Machine Learning for z/OS: An enterprise machine learning solution. 4 1.5 Unmatched capabilities of Machine Learning for z/OS . 4 1.5.1 Secure IBM Z platform for running machine learning with data in place. 5 1.5.2 Full lifecycle management of models . 5 1.5.3 Enterprise grade performance and high availability . 6 1.5.4 Flexible choice of machine learning languages and scoring engines . 7 1.5.5 Intuitive self-guided modeling capabilities. 7 1.5.6 Developer-friendly API interfaces for applications on the Z platform . 7 1.6 Value proposition of Machine Learning for z/OS. 7 Chapter 2. Planning . 9 2.1 Product installers. 10 2.2 Hardware and software requirements . 10 2.2.1 Prerequisites for z/OS. 11 2.2.2 Prerequisites for Linux . 11 2.2.3 Prerequisites for Linux on Z . 11 2.3 System capacity . 12 2.3.1 Basic system capacity. 12 2.3.2 Capacity considerations for training services . 13 2.3.3 Capacity considerations for scoring services . 13 2.3.4 Capacity considerations for performance . 13 2.4 Installation options on z/OS . 14 2.4.1 Option 1: Training and scoring services on the same LPAR. 14 2.4.2 Option 2: Training and scoring services on different LPARs. 16 2.4.3 Option 3: Training services that are on an LPAR and scoring services that are on an LPAR cluster. 16 2.5 User IDs and permissions . 17 2.6 Networks, ports, and firewall configuration . 19 2.6.1 Network requirements. 19 2.6.2 Ports . 20 2.7 Firewall configuration . 21 Chapter 3. Installation and customization . 23 3.1 Installation roadmap . 24 3.2 Installing and configuring IzODA. 25 © Copyright IBM Corp. 2018. All rights reserved. iii 3.2.1 Installing IzODA . 25 3.2.2 Configuring IzODA . 27 3.2.3 Verifying IzODA installation and configuration . 30 3.3 Configuring security . 35 3.3.1 Authorizing and authenticating users . 35 3.3.2 Creating and distributing an SSL certificate . 36 3.3.3 Configuring LDAP with SDBM for user authentication . 40 3.4 Installing and configuring Machine Learning for z/OS. 45 3.4.1 Installing machine learning services on z/OS . 45 3.4.2 Installing the scoring service in a CICS region . 50 3.4.3 Installing machine learning services on Linux or Linux on Z. 53 3.5 Configuring high availability and scalability. 58 3.5.1 Configuring TCP/IP for port sharing and load balancing . 59 3.5.2 Scaling an application cluster with extra compute nodes . 61 Chapter 4. Administration and operation . 63 4.1 Administration dashboard . 64 4.1.1 Accessing the administration dashboard . 65 4.1.2 Dashboard page . 65 4.1.3 Control Nodes page . 66 4.1.4 Services page . 67 4.1.5 Pods page. 68 4.1.6 Cluster Logs page. 69 4.1.7 Users page . 70 4.1.8 System Configuration page. 70 4.1.9 Scoring Services page . 71 4.1.10 Spark Clusters page . 71 4.2 Administering by using the administration dashboard. 72 4.2.1 Managing users and privileges . 72 4.2.2 Managing scoring services . 74 4.2.3 Managing remote Spark clusters . 76 4.2.4 Adding compute nodes . 77 4.2.5 Updating system configurations . 77 4.3 Administering by using commands on Linux or Linux on Z. 78 4.3.1 Monitoring system resource usage . ..