MLOps Infrastructure · Python
mo-lab-livive-net
A lightweight, self-hosted reference implementation of a complete machine learning operations (MLOps) lifecycle. Its purpose is to demonstrate how a model moves from a validated dataset through reproducible training, experiment tracking, registration, controlled deployment, production monitoring, drift detection, and retraining.
What it is
ModelOps Lab is a self-hosted reference implementation of a complete machine learning operations lifecycle, featuring synthetic data versioning, deterministic training, model registry, promotion gating, serving, monitoring, drift detection, and rollback.
Where it counts
- MLOps60%
- Data Science40%
- Machine Learning30%
How much of the project is about each field, as scoring counts it. It ranks in the field it is most about.
