Accelerate your business insights at scale with transactional AI on IBM z/OS
Transactional AI platform
Machine Learning for IBM z/OS® (MLz) is a transactional AI platform that runs natively on IBM z/OS. It provides a web user interface (UI), various application programming interfaces (APIs) and a web administration dashboard. The dashboard comes with a powerful suite of easy-to-use tools for model development and deployment, user management and system administration. |
Use with IBM z17™ and IBM Telum® II to deliver transactional AI capability. Process up to 282,000 z/OS CICS credit card transactions per second with a 4 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 option cuts response time by up to 20x and boosts throughput by up to 19x compared to an x86 cloud server averaging 60 ms network latency.2
Use trustworthy AI capabilities such as explainability while monitoring your models in real time for drift. Develop and deploy your transactional AI models on z/OS for mission-critical transactions and workloads with confidence.
Easily import, deploy and monitor models to achieve value from every transaction and drive new outcomes for your enterprise while maintaining operational service level agreements (SLAs).
Features
Key Capabilities
The new enhanced edition of Machine Learning for IBM z/OS delivers improved scoring performance, offers a new version of Spark and Python machine learning runtimes and includes a GUI-guided configuration tool and more. |
- Real-time inference: In-transaction scoring through native CICS and WOLA interface for CICS, IMS and BATCH COBOL applications and RESTful interface
- Various engines support: SparkML, Python, PMML, IBM SnapML, Watson Core Time Series
- Model lifecycle management: Guided UI, RESTful services
- Telum II: ONNX and IBM SnapML models
- Trustworthy AI: Explainability and drift monitoring
A shared JupyterHub environment allows multiple data scientists to build and train models together on the z/OS platform, improving collaboration and productivity.
Enhanced monitoring and clearer visualizations for explainability results help ensure models remain open, reliable and easy to interpret during production use.
MLz supports high-performance multiclass classification scoring by using the on-chip AI accelerator in IBM Z systems through Snap ML, improving model inference speed and efficiency.
MLz provides a secure, enterprise-grade platform for model development, deployment and management with web UI, APIs and integration with Spark and Python toolkits.
Specifications
Technical details
Machine Learning for IBM z/OS uses both IBM proprietary and open source technologies and requires prerequisite hardware and software.
- z17™, z16® or z15®
- z/OS 3.2 or 3.1
- IBM 64-bit SDK for z/OS Java™ Technology Edition version 11, 17, or 21
- IBM WebSphere Application Server for z/OS Liberty version 23.0.0.3 or later
- Db2® 13 for z/OS or later only if you choose Db2 for z/OS as the repository metadata database
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