The Elephant That Learns: How to use Machine Learning to Optimize PostgreSQL
April 21–22
PostgreSQL powers some of the most demanding workloads in the world, yet many performance problems remain difficult to detect, diagnose, and predict using traditional monitoring alone.
In this talk, we'll explore how machine learning can transform the way engineers understand and optimize PostgreSQL systems. We'll demonstrate practical ML techniques from anomaly detection and time-series forecasting to workload pattern recognition, and show where they can outperform conventional monitoring and tuning methods.
We'll demonstrate how ML models can potentially anticipate slowdowns, detect early signs of failure, recommend index changes, and help DBA's navigate complex performance behaviours using real world examples and open-source tooling. Attendees will leave with a clear roadmap for integrating ML into their PostgreSQL environments, whether for predictive maintenance, autonomous tuning, or large-scale performance analysis. The elephant can learn, and this session intends to show how to teach it.