Optimizing Software Performance with AI: Metrics, Models, and Best Practices
Conference: Symposium on Data Science and Statistics (SDSS) 2025
05/01/2025: 1:15 PM - 2:45 PM MDT
Lightning
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
Presenting Author
Srinivasa Rao Bittla, Adobe Inc
First Author
Srinivasa Rao Bittla, Adobe Inc
Tracks
Software & Data Science Technologies
Symposium on Data Science and Statistics (SDSS) 2025
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