AI on IBM Z® uses machine learning to convert data from every transaction into real-time insights.
Uncover insights and gain trusted, actionable results quickly without requiring data movement. Apply AI and machine learning to your most valuable enterprise data on IBM Z by using open-source frameworks and tools.
watsonx Code Assistant™ for Z is a generative AI-powered tool that provides an end-to-end application developer lifecycle. It includes application discovery and analysis, automated code refactoring and COBOL to Java conversion.
AI Toolkit for Z and LinuxONE consists of IBM® Elite Support and IBM Secure Engineering. These vet and scan open-source AI, serving frameworks and IBM-certified containers for security vulnerabilities, and validate compliance with industry regulations.
Machine Learning for z/OS® is an AI solution for users to build machine learning models by using any platform of choice and deploy those models within transactional applications while maintaining SLAs.
AI-infused transactional data
Experience an agile, efficient and secure enterprise data serving for the most demanding hybrid cloud and transactional and analytics applications.
Python AI Toolkit
Access a library of relevant open-source software to support current AI and machine learning workloads.
Accelerate TensorFlow Inference
Bring TensorFlow models that are trained anywhere and deploy them close to your business-critical applications on IBM Z by using IBM Integrated Accelerator for AI.
In-memory computing performance
Move forward with an in-memory compute engine and analytics run time that supports big-data languages such as JavaTM, Scala, Python and R.
Compile .onnx deep learning AI models into shared libraries
Compile compatible AI models into onnx format and run them on IBM Z with minimal dependencies, by using IBM Integrated Accelerator for AI seamlessly.
Popular open source tools
Use Anaconda on IBM Z and LinuxONE, and use industry-standard packages such as Scikit-learn, NumPy and PyTorch with cost-effective zCX containers.
Learn how to enable AI solutions in business-critical use cases, such as fraud detection and credit risk scoring, on the platform.
Discover low-latency AI on a highly trustworthy and secure enterprise system: the modernized IBM Mainframe.
¹ With IBM LinuxONE Emperor 4, process up to 300 billion inference requests per day with 1 ms response time by using a credit card fraud detection model
DISCLAIMER: Performance result is extrapolated from IBM internal tests. These tests run local inference operations in an IBM LinuxONE Emperor 4 LPAR (48 cores, 128 GB) on Ubuntu 20.04 (SMT mode). A synthetic credit card fraud detection model (https://github.com/IBM/ai-on-z-fraud-detection) is used with the Integrated Accelerator for AI. The benchmark was running with 8 parallel threads that are each pinned to the first core of a different chip. The lscpu command was used to identify the core-chip topology. A batch size of 128 inference operations was used. Results vary.