
Why AI Isn't Just More Software: A Guide to ML, MLOps, and Reinforcement Learning
Why can't you apply Agile sprints to an AI project? This episode dives into why ML development is 'fuzzy' and non-linear, unlike traditional software. We explore the 'nothing, nothing, something' problem that frustrates engineers and managers alike. Discover the real-world challenges of MLOps, from testing non-deterministic models to deployment. The conversation also breaks down Reinforcement Learning (RL), explaining how it learns from exploration, the high-stakes risks, and its role in training LLMs.







