MLOps
The planned outline of 15 modules. Lessons are written, run and reviewed before they are published.
Before this track
Planned outline
- Module 1
Foundations: what MLOps is and how to start
Coming soon - Module 2
Reproducible ML code and environments
Coming soon - Module 3
Experiment tracking with MLflow
Coming soon - Module 4
Data versioning and lineage
Coming soon - Module 5
Data validation and testing for ML
Coming soon - Module 6
Training pipelines and orchestration
Coming soon - Module 7
Feature pipelines and feature stores
Coming soon - Module 8
Model registry, packaging and formats
Coming soon - Module 9
Deployment and serving
Coming soon - Module 10
Monitoring models in production
Coming soon - Module 11
CI/CD and continuous training
Coming soon - Module 12
Platforms, scale and cost
Coming soon - Module 13
LLMOps: operating LLM features
Coming soon - Module 14
Security, governance and regulation
Coming soon - Module 15
Case study and interview practice
Coming soon
Official documentation
- mlflow.org/docs/latest (mlflow.org)
- developers.google.com/machine-learning/crash-course/production-ml-systems (developers.google.com)
More in AI and machine learning
- Machine Learning (scikit-learn) (coming soon)
- AI Literacy & Prompting (coming soon)
- Building LLM Applications (coming soon)
- Artificial Intelligence (classical AI and soft computing) (coming soon)
- All AI & ML tracks
- How we make lessons