ML Pipeline With MLflow — Experiment Tracking & Model Registry

I will build a complete ML pipeline with MLflow experiment tracking and model registry — covering data preprocessing, model training with hyperparameter logging, metric and artifact tracking per experiment run, model registration and stage promotion (Staging/Production), and a model serving endpoint loading the registered model. ML projects without experiment tracking are reproducibility disasters; understanding which hyperparameters, data version, and code produced the best model becomes impossible within weeks without a systematic tracking system.

The pipeline covers MLflow tracking server setup (local or remote), experiment creation, run logging (parameters, metrics, datasets, and model artifacts), model flavour registration, stage promotion workflow, MLflow Model Registry API for model loading in serving, and a documented pipeline with CLI arguments for retraining with different parameters. All experiments are logged to a structured MLflow UI your team can browse and compare.

Designed for data science teams building ML models who need reproducibility, experiment comparison, and a model registry enabling controlled promotion from development to production without losing track of what was trained on what data with what parameters.