Optimizing Software Performance with AI: Metrics, Models, and Best Practices

In today's fast-paced digital ecosystem, Site Reliability Engineers (SREs), Quality Engineers, and Performance Engineering teams face increasing challenges in ensuring software systems remain fast, scalable, and resilient under dynamic workloads. Traditional performance engineering techniques often fall short in proactively identifying and mitigating performance issues, leading to costly downtime and degraded user experiences. AI-driven performance engineering is transforming how organizations approach performance testing, monitoring, and optimization by leveraging machine learning models to predict bottlenecks, automate anomaly detection, and enhance system reliability.

This session will dive into AI-powered performance engineering strategies, highlighting key performance metrics such as latency, throughput, error rates, anomaly detection accuracy, and auto-remediation effectiveness. We will explore best practices for integrating AI into performance workflows, covering areas like intelligent workload modeling, self-healing systems, adaptive load balancing, and real-time observability.

AI-Driven Performance Engineering

Machine Learning in Performance

AI in DevOps Performance Monitoring

Cloud-Native Performance Engineering with AI

AI for Performance Bottleneck Detection

AI-Enhanced Application Performance Monitoring 

Software & Data Science Technologies

Symposium on Data Science and Statistics (SDSS) 2025