MLOps
Write-upMLflow Model Lifecycle
A compact path from tracked experiment to registered model and served prediction.
Context: MLOps and model lifecycle learning project
Summary
This learning project follows a machine-learning model beyond training: experiments and parameters are tracked, selected runs enter a model registry, versions can be promoted, and the chosen model can be loaded for local inference or served through MLflow. It provides a small, inspectable example of the lifecycle around a scikit-learn model.
From a training run to a served model
Experiment
Parameters, metrics, and artifacts are tracked
Comparison
Recorded runs support model selection
Registry
The chosen artifact is versioned and promoted
Inference
Applications load the registered model
Serving
MLflow exposes a prediction endpoint
What the system handled
Demonstrates experiment tracking, registry usage, model promotion, local inference, and model serving.
Technology
Stack
Areas
Conclusions
- Tracking parameters, metrics, and artifacts makes model selection reproducible from recorded evidence.
- The registry creates a clear boundary between an experimental run and a model version selected for inference or serving.
- The project is deliberately compact; its value is demonstrating the lifecycle clearly before adding production concerns such as monitoring, automated validation, and deployment gates.