Modern ML teams don’t just build models—they build reliable, reproducible, and cost-efficient workflows. In this course, you’ll learn the core development skills that make ML projects scale in real engineering environments. You’ll practice managing experiments with clean Git branching strategies, creating fully reproducible environments using Poetry, and monitoring CPU, GPU, and memory usage to avoid failures and control cloud costs. Through videos, hands-on activities, and a guided lab, you’ll version notebooks and artifacts, lock dependencies for stable builds, and analyze resource logs from VS Code Remote to prevent OOM events and runaway grid searches. By the end, you’ll be able to structure ML codebases more effectively, deliver reproducible experiments to teammates, and run cost-aware training workflows that fit both performance and budget constraints.