IBM Machine Learning for z/OS® (MLz), formerly IBM Watson® Machine Learning for z/OS, is a transactional artificial AI solution that runs natively on IBM Z®. It provides a web user interface (UI), various APIs and a web administration dashboard with a powerful suite of easy-to-use tools for model development and deployment, user management and system administration.
Easily import, deploy and monitor models to achieve value from every transaction and drive new outcomes for your enterprise while maintaining operational SLAs.
For greater flexibility, Machine Learning for z/OS includes two editions:
All IBM Machine Learning for IBM z/OS editions can run as a stand-alone solution or infuse it into your enterprise AI capability as a scalable platform.
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Visualized explanations of AI inferences can be natively accessed in MLz
MLz Core
MLz Enterprise
Use the unprecedented power of IBM z16™ and the Telum™ AIU with the Machine Learning for z/OS software solution to deliver transactional AI capability. Process up to 228 K z/OS CICS credit card transactions per second with 6 ms response time, each with an in-transaction fraud detection inference operation that uses a Deep Learning Model.1
Colocate applications with inferencing requests to help minimize delays caused by network latency. This delivers up to 20x lower response time and up to 19x higher throughput versus sending the same inferencing requests to a x86 cloud server with 60 ms average network latency.2
Use trustworthy AI capabilities like explainability and monitor your models in real time for drift to develop and deploy your transactional AI models on z/OS for mission-critical transactions and workloads with confidence.
An enhanced edition that delivers improved scoring performance, a new version of Spark and Python machine learning runtimes and includes a GUI-guided configuration tool and more.
A lightweight version of WMLz providing the essential services that are REST-API-based for machine learning operations including online scoring capabilities on IBM Z.
Configuration experience
Guided UI
Scripts, /OSMF Workflow
Repository database
Db2® for IBM z/OS, built-in (Derby for z/OS)
Db2 for IBM z/OS, built-in (Derby for z/OS)
Scoring engine
Spark, Python, PMML, IBM Snap ML, Watson Core Time Series
Spark, PMML, IBM Snap ML, Watson Core Time Series
Inference interface
In-transaction scoring with native interfaces for CICS and IMS, RESTful interface
RESTful interface
Model lifecycle management
Guided UI, RESTful services
RESTful services
Supported AI model formats
Spark, Python, PMML, ONNX
Spark, PMML
z16 on-chip AI acceleration
ONNX and IBM Snap ML models
IBM Snap ML Models
AI model training tool
Integrated JupyterHUB
Trustworthy AI
Explainability and drift detection
Try this lightweight, no-cost option to experience IBM Machine Learning for z/OS, enabling in-transaction scoring for deep learning models. This capability can deliver significant AI value in critical business areas such as fraud detection, customer churn, loan approval and operational performance. Embed deep learning models in your transactional applications on IBM Z, particularly when milliseconds matter.
Machine Learning for z/OS uses both IBM proprietary and open source technologies and requires prerequisite hardware and software.
Identify operational issues and avoid costly incidents by detecting anomalies in both log and metric data.
Access a library of relevant open source software to support today's AI and ML workloads.
Leverage a security-rich and scalable operating system for running mission-critical applications.
Enhance availability, security and resiliency while improving performance and business results.
Get high-speed data analysis for real-time insight under the control and security of IBM Z.
Learn how AI enhances usability, improves operational performance and maintains the health of IBM Db2 systems.
1 DISCLAIMER: The performance result is extrapolated from IBM internal tests running a CICS credit card transaction workload with inference operations on an IBM z16. A z/OS V2R4 LPAR configured with 6 CPs and 256 GB of memory was used. Inferencing was done with Machine Learning for z/OS 2.4 running on WebSphere Application Server Liberty 21.0.0.12, using a synthetic credit card fraud detection model (https://github.com/IBM/ai-on-z-fraud-detection) and the Integrated Accelerator for AI. Server-side batching was enabled on Machine Learning for z/OS with a size of 8 inference operations. The benchmark was run with 48 threads performing inference operations. Results represent a fully configured IBM z16 with 200 CPs and 40 TB storage. Results might vary.
2 DISCLAIMER: Performance results are based on an IBM internal CICS OLTP credit card workload with in-transaction fraud detection running on IBM z16. Measurements were done with and without the Integrated Accelerator for AI. A z/OS V2R4 LPAR configured with 12 CPs, 24 zIIPs and 256 GB of memory was used. Inferencing was done with Machine Learning for z/OS 2.4 running on WebSphere Application Server Liberty 21.0.0.12, using a synthetic credit card fraud detection model (https://github.com/IBM/ai-on-z-fraud-detection). Server-side batching was enabled on Machine Learning for z/OS with a size of 8 inference operations. Results might vary.