MLflow Live Demo | Experiment Tracking and Model Versioning

Topics Covered:
1. Train a Basic classifier using Random Forest
2. Create Experiment-Basic classifier
3. Log metrics, model, and other artifacts
4. Tune model using hyperparameter tuning using Randomized Search CV
5. Create another experiment in MLFlow- Optimised classifier
6. Use SQLite as the backend database for model registry
7. Explore MLflow UI, interpret experiments and runs

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