ABOUT THIS EPISODE
Why do AI projects feel so unpredictable? If you've ever been frustrated by a machine learning project that shows "nothing, nothing... then suddenly, something," this episode is for you. We move past the hype to explore the "fuzzy" reality of building with AI and why it’s a fundamentally different discipline from traditional software engineering.
Discover why you can't just apply Agile sprints to ML development and why models need to be "massaged and babied" rather than simply "built." We break down the practical engineering challenges of MLOps, from the difficulties of testing non-deterministic systems (where "pass/fail" doesn't apply) to the complexities of safely deploying them.
We also go deep on Reinforcement Learning (RL), a powerful but high-stakes branch of AI. You'll learn:
What makes RL unique (its "agency to explore").
Why live testing in industrial settings "could be catastrophic."
How "Offline RL" allows us to train powerful agents from simple log files.
The surprising role RL plays in training the large language models (LLMs) we use every day.
Tune in for a practical, engineering-focused look at what it really takes to get AI from a concept to a reliable, production-ready product.
