MLOps
Taking machine learning systems to production: reproducibility, automation, deployment, and monitoring.
Prerequisites: the Machine Learning course, basic command line and Git.
Syllabus
- Introduction
- The ML Lifecycle (planned)
- Experiment Tracking (planned)
- Data and Model Versioning (planned)
- Pipelines and Automation (planned)
- Model Deployment (planned)
- Monitoring and Observability (planned)