IBM Machine Learning for z/OS® (MLz), formerly IBM Watson Machine Learning for z/OS, is a transactional artificial intelligence solution that runs natively on IBM Z®. It focuses on accomplishing specific tasks, like scoring transactions in a highly efficient and optimized manner, often following predefined rules or protocols. 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.
Infuse AI models within your z/OS transactional applications and deliver real-time business insights at scale. 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:
- IBM Machine Learning for IBM z/OS Enterprise Edition – Full lifecycle end-end AI platform with enterprise AI features like native CICS and IMS scoring interfaces, Python and Spark scoring services, ONNX and Deep Learning Compiler support and Trustworthy AI features like Explainability.
- IBM Machine Learning for IBM z/OS Core Edition – a lightweight version of MLz providing the essential services that are REST-API-based for machine learning operations including online scoring capabilities on IBM Z.
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.
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
Leverage trustworthy transactional AI capabilities like explainability and monitor your models in real time for drift, fairness or bias detection and robustness to develop and deploy your transactional AI models on z/OS for mission-critical transactions and workloads with confidence.
Try this lightweight, no-charge 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.
With the update to version 3.1, MLz is offering more flexibility to clients and solution providers with the introduction of 2 new offerings: Enterprise Edition and Core Edition.
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.
GUI configuration
UI (for model management and deployment, admin dashboard)
Repository database (built-in and Db2® for z/OS)
AI model training tool (integrated Jupyter Notebook)
Spark ML runtime
Python ML runtime
SparkML and PMML scoring runtime
Python and ONNX scoring runtime
Inference Services–RESTful interface
Inference services–native interface
Integrated in-transaction scoring (CICS and IMS apps)
Machine Learning for z/OS uses both IBM proprietary and open-source technologies and requires prerequisite hardware and software.
- z16™, z15®, z14, z13® or zEnterprise® EC12 system
- z/OS 3.1, 2.5 or 2.4
- IBM 64-bit SDK for z/OS Java™ Technology Edition version 8 SR7, 11.0.17 or later
- IBM WebSphere Application Server for z/OS Liberty version 22.0.0.9 or later
- Db2 12 for z/OS or later only if you choose Db2 for z/OS as the repository metadata database
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.
Footnotes
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.