MLOps Foundations

What you will be able to do

• You will be able to explain what MLOps is and make the case for why traditional DevOps practices are not enough for machine learning systems.
• You will be able to describe how the data, model, and code triad creates hidden technical debt that ordinary software delivery does not plan for.
• You will be able to map the end-to-end machine learning lifecycle and name the owner, inputs, and common failure points at each stage.
• You will be able to coordinate the roles on an ML team, from data scientists to platform engineers, using shared MLOps language and clean handoffs.
• You will be able to apply reproducibility practices by versioning code, data, models, and environments so any result can be recreated on another machine.
• You will be able to use experiment tracking and pipeline concepts to turn one-off notebook work into repeatable, auditable runs.
• You will be able to score a team against a recognized MLOps maturity model, such as the Google and Microsoft frameworks, and name the gaps holding it back.
• You will be able to draft a practical roadmap that moves a team from manual steps toward automated pipelines and continuous training.

What is inside

4 modules, 8 lessons. Each module ends in a graded quiz and most carry an assignment.

1. 01

   Why MLOps Exists

   Machine learning systems fail in ways ordinary software does not, because their behavior comes from shifting data and trained models, not from code alone. This module makes the case for MLOps: what it is, the problems it solves, and how it grew out of DevOps to meet the demands of ML. By the end you can explain why getting a model to work once is only the start of the reliability problem, and why data, model, and code must be managed together.

   2 lessons · 5 quiz questions

2. 02

   The End-to-End ML Lifecycle

   A working map of every stage a machine learning system moves through, from framing the problem to monitoring it in production, plus the roles that own each step and the seams where projects usually break.

   2 lessons · 5 quiz questions · assignment

3. 03

   Reproducibility and Versioning

   Reproducibility is the core engineering discipline of MLOps: if you cannot recreate a result, you cannot trust it, debug it, or improve it. This module shows how to version code, data, models, and environments together, and how experiment tracking and pipelines turn a one-off notebook into a process any teammate can rerun and audit.

   2 lessons · 5 quiz questions

4. 04

   The MLOps Maturity Model

   Not every team needs the same level of automation, but every team should know where it stands. This module walks the recognized maturity models from Google and Microsoft, traces the path from manual steps to CI/CD and continuous training, and ends with you drafting an honest roadmap that moves a real team one level forward.

   2 lessons · 5 quiz questions · assignment