TechnologyMLOps: From Notebook to Production
Take machine learning models from a notebook to a tested, deployed and monitored service.
Basic Python and ML knowledge helps.
What you'll learn
Curriculum
1Week 1 · Beginner
ML Recap, Reproducible Projects and Experiment Tracking
5 lessons
Week 1 · Beginner
ML Recap, Reproducible Projects and Experiment Tracking
2Week 2 · Intermediate
Model Registry, Data Versioning and Pipelines
5 lessons
Week 2 · Intermediate
Model Registry, Data Versioning and Pipelines
3Week 3 · Advanced
Model Serving, Containers and CI/CD for ML
5 lessons
Week 3 · Advanced
Model Serving, Containers and CI/CD for ML
4Week 4 · Advanced
Monitoring, Drift and the Final Project
5 lessons
Week 4 · Advanced
Monitoring, Drift and the Final Project
About this course
A 30-day MLOps course that builds from beginner to advanced. Recap the ML workflow, make projects reproducible, track experiments and register models with MLflow, version data with DVC, build pipelines, serve models with FastAPI and Docker, add CI/CD for ML, and monitor for drift, then build a final notebook-to-production project. No certificate yet.
Requirements
- Python and basic pandas
- Basic machine learning with scikit-learn (train, test, evaluate)
- A free GitHub account; Docker helps from Week 3
Who is this for
- Data scientists who want their models used in production
- Students aiming for ML engineer or MLOps roles
- Software engineers working alongside ML teams
30 days of expert content
Hands-on projects & assignments
Lifetime access
No certificate yet
Mobile & desktop access