MLOps
MLOps applies DevOps principles to machine learning systems, covering experiment tracking, containerization, and continuous integration practices.
Contents:
- Docker — Containerized deployment, image building, GPU containers
- TensorBoard — Training visualization, metrics monitoring
- Weights & Biases — Experiment management, hyperparameter sweeps, team collaboration
- MLflow — Model registry, experiment tracking, model serving