Module 1 - MLOps Foundations
"Most ML projects fail not because of bad models, but because nobody built the system around the model."
Machine learning has a production problem. A model that achieves 94% accuracy in a notebook and a model that reliably serves predictions to millions of users while degrading gracefully over time are separated by an enormous engineering gap. MLOps is the discipline that closes that gap.
This module builds the mental model you need before touching a single tool. You will learn what MLOps actually is (not the marketing version), why production ML is categorically different from research ML, and how mature teams structure the entire lifecycle from raw data to live predictions.
What You Will Learn
Lessons at a Glance
| # | Lesson | Core Idea |
|---|---|---|
| 01 | The MLOps Lifecycle | End-to-end view: 9 components, maturity levels 0–3, what makes ML deployment different |
| 02 | Reproducibility in ML | Four layers: environment, data, code, model - seed management, DVC, Docker |
| 03 | ML Pipelines | Notebook → production pipeline migration, DAG orchestration, pipeline testing |
| 04 | Feature Engineering Fundamentals | Point-in-time correctness, training-serving skew, feature contracts |
| 05 | MLOps Tooling Landscape | Full stack mapped to problems, build vs buy, avoiding tool sprawl |
Key Concepts Introduced
- MLOps maturity levels - the four stages teams move through from ad-hoc to fully automated
- The hidden technical debt paper - Sculley et al. (2015), Google's foundational argument for treating ML as a systems problem
- Training-serving skew - the silent killer of production ML accuracy
- DAG-based pipelines - directed acyclic graphs as the organizational unit of reproducible ML workflows
- Point-in-time correctness - why features computed at training time must exactly match features computed at serving time
Prerequisites
This module assumes you can train a machine learning model (scikit-learn, PyTorch, or similar) and have basic Python proficiency. You do not need prior DevOps or cloud experience - those come in later modules.
Why Start Here?
Every experienced ML engineer has a story: the model that worked in the notebook but broke in production, the metric that looked great until it silently degraded, the experiment they couldn't reproduce. These are not bad luck - they are predictable consequences of skipping the systems thinking that MLOps provides.
Start here. Build the foundation. Everything else in the MLOps track - experiment tracking, data versioning, model serving, monitoring - will make far more sense once you have the lifecycle map in your head.