In this episode of the Data Science Salon Podcast, we sit down with Vishnupriya Devarajulu, a Software Engineer specializing in AI- and ML-driven performance optimization for large-scale enterprise systems. With deep expertise in backend engineering, system diagnostics, and intelligent test automation, Priya is redefining how organizations build systems that don’t just respond—they anticipate.
Priya walks us through her work designing adaptive frameworks that use machine learning to forecast system bottlenecks, improve latency, and optimize performance in high-stakes environments like finance.

Key Highlights:
Priya explains how she transforms traditional automation into self-learning, AI-powered frameworks using models like Random Forest to proactively identify and solve system issues.
A deep dive into building ML-integrated performance pipelines that can adapt over time, dynamically suggest test scenarios, and drive smarter, faster, and more resilient systems.
Insights into how predictive performance engineering is being applied in domains where speed and reliability are non-negotiable—and how to architect systems for it.
Priya shares her perspective as a speaker and published researcher, and where she sees the future of intelligent infrastructure and AI-powered diagnostics heading next.

Whether you're a systems engineer, ML practitioner, or enterprise leader exploring how AI can boost operational efficiency, this episode offers a powerful look at what happens when machine learning meets performance engineering.
🎧 Tune in to Episode 49 and discover how Priya is building the future of intelligent systems—one prediction at a time.