Machine Learning and AI Integration in Db2 for z/OS

In today’s data-driven world, the ability to harness the power of machine learning (ML) and artificial intelligence (AI) is essential for organizations aiming to stay competitive. With the introduction of Db2 for z/OS Version 13 and subsequent function levels, IBM has made significant strides in integrating ML and AI capabilities directly into the Db2 ecosystem, transforming the way businesses leverage their data.

SQL Data Insights

Perhaps the single most important new AI capability added to Db2 13 for z/OS is SQL Data Insights (SDI). I have written about this before and if you are interested in a more thorough discussion of SDI, check out this article on elnion.

At a high level though, SDI enables data scientists and analysts to run advanced analytics directly on data residing in Db2 without the need for extensive data movement. By minimizing data transfer, organizations can reduce latency and improve the efficiency of their workflows.

The initial support for SDI in Db2 13 for z/OS FL600 included three AI functions: AI_SIMILARITY, AI_SEMANTIC_CLUSTER and AI_ANALOGY. Function level 504 added a fourth: AI_COMMONALITY.

Python Support

Python is the dominant programming language for AI and ML because of its simplicity, readability, and vast ecosystem of libraries. It offers clear syntax allowing data scientists and developers to focus on solving problems rather than wrestling with complex code structures. This makes it ideal for rapid prototyping of AI models. Rich frameworks such as TensorFlow, PyTorch, and others provide ready-to-use tools for data preparation, model training, and evaluation, significantly reducing development time. Moreover, Python’s large, active community continually contributes new algorithms, techniques, and integrations, ensuring that it stays at the forefront of AI and ML innovation. This combination of usability, flexibility, and ecosystem maturity has made Python the de facto standard for building, deploying, and operationalizing AI and ML solutions across industries.

With Python being so important to data scientists, it stands to reason that IBM should support it in Db2 for z/OS. And they do! Python support for Db2 for z/OS was delivered with the IBM Db2 AI for z/OS and the Db2 for z/OS Python driver as part of the IBM Db2 for z/OS “Data Server Driver for ODBC, CLI, and .NET” family.

• IBM Db2 AI for z/OS (Db2ZAI) is an advanced solution designed to enhance the operational performance, reliability, and efficiency of Db2 for z/OS systems. By leveraging machine learning (ML) and artificial intelligence (AI), it improves many aspects of Db2 management. We will discuss it in a little more detail in the next section.
• The Python driver is IBM's official database connectivity driver that allows Python applications to connect to and interact with IBM DB2 databases. It delivers connectivity not just for Db2 for z/OS, but also for other IBM database products including DB2 for Linux/Unix/Windows, DB2 for i (AS/400), and IBM Informix.

So, Python support became generally available via IBM Db2 for z/OS Distributed Data Facility (DDF) using the IBM Data Server Driver for Python, which is the same Python driver used for Db2 LUW, but configured to connect over DRDA to Db2 for z/OS.

This wasn’t tied to a specific Db2 function level—rather, it was an enhancement to the client connectivity stack and supported back to Db2 11 for z/OS with the right PTFs. Of course, as of this December (2025) Version 13 will be the only supported version of Db2 for z/OS.

Machine Learning Enhanced Optimization

The Db2 optimizer can also benefit from an infusion of AI. Optimization improvement is a benefit of IBM’s Db2 AI for z/OS, an add-on solution that uses AI/ML to elevate system operations and performance.

IBM Db2 AI for z/OS continuously analyzes workload patterns, system metrics, and SQL execution behavior to recommend or automatically apply optimizations—such as selecting better access paths, tuning buffer pools, or adjusting configuration settings to reduce CPU usage. By learning from an organization’s actual Db2 workload over time, it adapts its recommendations to evolving data and usage patterns, helping maintain consistent performance without constant manual tuning.

In addition, Db2 AI for z/OS can assist in workload management, anomaly detection, and operational decision-making, giving DBAs intelligent, data-driven insights to run large-scale mainframe database systems more efficiently. By incorporating machine learning into key processes it can help to reduce CPU usage, optimize SQL query plans and concurrency, and detect and resolve anomalies and root causes.

Indeed, the AI-driven operational support of Db2 AI for z/OS goes beyond using AI in SQL queries. It is focused on keeping Db2 for z/OS environments running optimally and proactively, enhancing system resiliency and availability.

Summing Things Up

IBM continues to integrate machine learning and AI capabilities into Db2 for z/OS. By empowering organizations to leverage their data for predictive analytics and advanced machine learning, IBM is helping businesses unlock new opportunities and drive smarter decision-making. As these technologies continue to advance, the potential for innovation and growth in the data landscape is limitless. Embrace the future of data with Db2 for z/OS and unleash the power of AI and machine learning in your organization today!