Written by the AgileFever editorial team. Program details were checked against the live program pages in October 2026. We run this program. The module list comes from its published curriculum.
Most MLOps curricula are tool lists. This walkthrough pairs each module with the lab you do, so you can see whether the program matches what your team needs to run in production.
Module by module, with the lab
| # | Module | Hands-on lab |
|---|---|---|
| 1 | Python programming foundations | Small programs such as a bank account or inventory system |
| 2 | Python libraries for data science (NumPy, Pandas, Matplotlib, Seaborn) | Exploratory analysis of a real dataset with visual dashboards |
| 3 | Statistics for data science | Compute statistical measures in Python |
| 4 | Machine learning, supervised and unsupervised | Train classification and regression models with scikit-learn |
| 5 | Deployment with VS Code, GitHub and Streamlit Cloud | Deploy a trained model as a web app |
| 6 | Code versioning with Git | Create a repository, manage branches, resolve merge conflicts |
| 7 | CI/CD with GitHub Actions | A CI pipeline that tests and builds the ML project |
| 8 | Experiment tracking with MLflow | Log experiments and compare model runs |
| 9 | Data versioning with DVC | Track a dataset and reproduce an experiment |
| 10 | DagsHub integration (Git, DVC and MLflow) | Push an integrated ML project to DagsHub |
| 11 | Docker and containerization | Containerize an inference application |
| 12 | Apache Airflow | Build an automated training DAG |
| 13 | Kubernetes | Deploy an ML container on a cluster |
| 14 | Monitoring with Grafana and Prometheus | Dashboards and alerts for ML metrics |
| 15 | AWS, Azure, GCP and Vertex AI for MLOps | Deploy and scale models on cloud infrastructure |
| 16 | LLMOps | Build a RAG chatbot and deploy it as an API |
| 17 | Agentic AI Ops | Build and monitor a tool-using agent |
Count of the 17 modules
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What you can skip mentally, depending on your background
| Your background | Likely review | Likely new |
|---|---|---|
| DevOps or platform engineer | Modules 6, 7, 11, 13 and 14 | Modules 3, 4, 8 to 10 and 16 to 17 |
| Data scientist or ML engineer | Modules 1 to 4 | Modules 6 to 15, then 16 and 17 |
| Backend developer | Modules 1, 5, 6 | Most of 2 to 4, 8 to 10, and 12 |
The program page says that if you already run Kubernetes and MLflow in production, much of it will be review. That fits our reading. The capstone is built on Google Cloud, so if your company runs on AWS or Azure, ask how much of the capstone carries over.
What it does not cover
- Training large models from scratch or deep research topics.
- Data engineering for large warehouses. The Data Engineer BootCamp is the closer match.
- One cloud in great depth. Module 15 introduces AWS, Azure, GCP and Vertex AI, and the capstone runs on Google Cloud.
It needs basic Python, and DevOps experience helps but is not required. Read our complete guide, the cost breakdown and the guide for DevOps engineers for more. The program page is the MLOps, LLMOps and Agentic AI Ops BootCamp.
Questions people ask
Does it teach machine learning from the beginning?
Modules 1 to 4 cover Python, data libraries, statistics and core ML, enough to understand what you are deploying. They are not a substitute for a full ML course.
Is LLMOps a separate course?
No. It is module 16, followed by agent operations in module 17, within the same 60 hours.
Are sessions recorded?
Yes. Live sessions are recorded.
