Learn about integrating AI into IBM Db2® for z/OS to improve operational productivity

IBM Watson Machine Learning for z/OS details

Flexible model development

Give data science teams the flexibility to build, train and evaluate models using their Integrated Development Environment (IDE) of choice. Or use the IBM Watson® Machine Learning for z/OS® extensive model-building features that are based on enterprise-grade open source software.

Improved productivity

Optimize data scientist productivity through extensive IBM Watson Machine Learning for z/OS model-building features. This product offers several model-building modes including notebooks, visual builders, wizards and enhanced intelligence applied to data scientist activities. Automatically normalize, handle missing values and generate data features to make even novice data scientists into experts.

Enterprise-ready AI model deployment

Operationalize predictive models within transaction applications, without significant overhead, enabling real-time insight at the point of interaction. This product offers several scoring approaches including RESTful APIs and Java and CICS integration, optimized for the highest security and performance levels on IBM Z®.

Enhanced model accuracy

Enable data scientists and engineers to schedule continuous re-evaluations of new data to monitor model accuracy over time and be alerted when performance deteriorates. Automatically refresh models to maintain model accuracy with confidence.

Production-ready machine learning

Deliver essential model versioning, auditing and monitoring as well as high availability, high performance, low latency and machine learning model automation (machine learning as a service).

Quick-start solution templates

Offer essential foundational templates for common business requirements to bootstrap your machine learning efforts. Solution templates demonstrate how machine learning can run alongside your application infrastructure to add value to key business areas including fraud detection, loan approval and IT operational analytics.

Technical details

Technical specifications

What's new?

  • Upgrade of key system components, including the upgrade to bash 4.3.48, node.js 8.16.0, and Apache Toree 03
  • Support of XGBoost 0.82 and the ability to train and deploy XGBoost models on z/OS
  • Enhancements in online scoring service through the support of custom MLeap libraries and vector input type of CICS LINK programming interface
  • Enhancements in up-and-running experience through simplified installation and configuration, including automated configuration verification and CA-signed certificate generation

Software requirements

  • Watson Machine Learning for z/OS Base: z/OS 2.3 or 2.2: For z/OS 2.3, apply PTFs UA98440, UI61308, UI61376, UI61747, and UI61375
  • For z/OS 2.2, apply PTFs UA98441, UI62788, UI46658, UI62416, and UI6241 z/OS Integrated Cryptographic Service Facility (ICSF) z/OS OpenSSH IBM SDK for Node.js 8.16.1
  • IBM 64-bit SDK for z/OS Java (Java 8 SR4 FP6) or later. IBM Open Data Analytics for z/OS (IzODA) 1.1: Install z/OS Spark 2.2.0 or 2.3.0 (FMID HSPK120) with PTFs UI65007 and UI65177 applied.
  • Install z/OS Anaconda 3.6.0 or 3.7.0 (FMID HANA110) with PTF UI65107 applied. Optionally, install z/OS Data Service 1.1 (FMID HMDS120) with PTF UI63532 applied. Db2 V11 for z/OS or later
  • Watson Machine Learning for z/OS V2.1.0.2 for Linux: Watson Machine Learning IDE on Linux on Z 1 (for single-node configuration) or 3 (for three-node configuration) s390x 64-bit servers
  • Red Hat Enterprise Linux Server 7.4, 7.5, or 7.6 or Ubuntu 18.04. openJDK 1.8.0 or later Watson Machine Learning for z/OS IDE on Linux on x86 1 (for single-node configuration) or 3 (for three-node configuration) x86 64-bit servers and Red Hat Enterprise L

Hardware requirements

  • z14, IBM z13®, or IBM zEnterprise® EC12 system.1 GCP, 4 zIIPs, 100 GB memory, 100 GB Dasd/disk space
  • Watson Machine Learning for z/OS IDE on Linux on Z LPAR of a z14, z13, IBM z13s®, zEnterprise EC12, zEnterprise BC12, LinuxOne Emperor, or LinuxOne Rockhopper system 3 IFLs, 48 GB memory, 50 GB of primary storage, 300 GB secondary storage
  • Watson Machine Learning for z/OS IDE on Linux on x86 8 cores of CPU, 48 GB memory, 50 GB of primary storage, 300 GB secondary storage

Next Steps

See how it works