Production ML and AI operations

Deploy, release, scale and operate ML and LLM systems in production.

This is you

You like to keep systems running: you work in operations, DevOps or the back end, or you want to. You want AI systems that stay healthy, fast and affordable after launch.

Where you are going

An MLOps or LLMOps engineer

Deploy models and LLM features with repeatable releases
Monitor quality, latency and cost after launch
Scale APIs and AI workloads when traffic grows
Respond to incidents, and hand a system over to another team

3 stages
16 courses, 16 available now
About 210–285 hours of study
16 certificates of completion

How much code you need

No code
Read code
Some code
Code is central

Code is central: you write pipelines, configuration and tests. If you already work in operations, many tools will look familiar.

Your route, stage by stage

Each stop is a course. Its shape tells you how much of it to take.

Full course. The whole course, with its practice. It leads to a certificate of completion.
Concepts. Only the concept modules, read without running code. They lead to a concepts certificate.
Optional. Useful for some people. You do not need it, and it is not counted in the time.

You, today
Stage 1 Foundationsabout 51–69 h
1. AI Career Foundations: Roles, Skills and Roadmaps
  Full course4–6 h
  See what operations work looks like in AI teams.
2. Python for Practical AI Engineering
  Full course18–24 h
  Python for scripts, services and tests.
3. Git and Team Development Essentials
  Full course5–7 h
  Every release starts in Git.
  Focus: If you already have these skills, do its final assignment instead.
4. Essential Mathematics and Statistics for AI
  Full course12–16 h
  The statistics behind monitoring alerts.
  Focus: If you already studied this, do its final assignment instead.
5. SQL and Data Preparation for AI
  Full course12–16 h
  The data that models are trained and tested on.
  Focus: If you already studied this, do its final assignment instead.
Stage 2 The systems that you will runabout 76–100 h
1. Machine Learning: From Problem to Reliable Model
  Full course26–32 h
  Know what a model is before you operate one.
  Focus: If you already studied this, do its final assignment instead.
2. LLMs: How They Work and How to Choose One
  Full course8–12 h
  How LLMs are delivered, and what they cost.
3. Building Reliable Applications with LLM APIs
  Full course22–30 h
  The LLM features that you will deploy.
4. Evaluating and Testing AI Systems
  Full course20–26 h
  Quality checks that run before each release.
Stage 3 Productionabout 83–116 h
1. Build and Deploy Your First API
  Full course11–15 h
  Serve a model behind an API.
  Focus: If you already have these skills, do its final assignment instead.
2. Docker and Cloud Essentials for AI Developers
  Full course7–10 h
  Containers and the cloud.
3. Deploying AI Models for Real Users
  Full course8–12 h
  Serve models to real users.
4. Databases and Storage for AI Applications
  Full course16–22 h
  Databases and storage for AI systems.
5. CI/CD, Testing and Safe Releases
  Full course8–12 h
  Automate tests and safe releases.
6. Scaling APIs and AI Workloads
  Full course17–23 h
  Handle more traffic without failures.
7. MLOps and LLMOps: Operating AI in Production
  Full course16–22 h
  The core of this path: operate AI over time.
You arriveAn MLOps or LLMOps engineer

Earn certificates as you go

You do not need to finish the whole path to show what you learned. Each full course gives a certificate of completion, and each concepts course gives a concepts certificate. Share each one when you get it.

When you are ready, prepare for the interview: Interview preparation: Machine Learning Engineer

Make it my path All learning paths

Choose this goal in your profile. My learning then shows your progress on this route, and the weeks it takes at your pace.