01Module 1 - MLOps FoundationsUnderstand what MLOps is, why it exists, and how to think about operationalizing machine learning systems in production.02The MLOps LifecycleUnderstand the end-to-end MLOps lifecycle, maturity levels 0–3, the nine components of production ML, and why ML deployment is categorically different from software deployment.03Reproducibility in MLLearn the four layers of ML reproducibility - environment, data, code, and model - and how to achieve each in practice with Docker, DVC, MLflow, and seed management.04MLOps vs DevOpsHow MLOps extends DevOps principles to handle the unique challenges of data, model quality, and concept drift that traditional software CI/CD cannot address.05The ML LifecycleThe complete end-to-end lifecycle of a machine learning model, from problem definition through deployment, monitoring, and eventual retirement - with feedback loops, governance, and retraining triggers.06Technical Debt in ML SystemsThe seven categories of hidden technical debt unique to machine learning systems - entanglement, hidden feedback loops, pipeline jungles, configuration debt, and how to detect and remediate them.
