AI writes your business logic now. But deploy that code to production, hit it with real traffic, and something breaks. Without understanding performance principles, you can’t even ask the right questions because you don’t know what to measure, what context to provide, or what information to produce to meaningfully debug the problem, with or without AI.
Diagnosing bottlenecks, understanding what happens between your application and the database under load, reasoning about concurrency and contention when you try to scale, this is the engineering depth that turns guessing into systematic problem solving.
In this hands-on seminar, we’ll work on a live Python service under real load, systematically diagnosing, fixing and scaling it with measurable before and after results and principles that apply to any language and stack. AI made code writing easy, this seminar focuses on what’s still hard.
