Model Tracking & Registration

During the experimental phase of Machine Learning, Data Scientists train hundreds of models, testing different algorithms (Random Forest vs XGBoost), different hyperparameters (max_depth=5 vs max_depth=10), and different data processing pipelines.

If this experimentation is tracked in an Excel spreadsheet or just kept in the developer's head, the project will fail. You will not be able to reproduce your best result once you forget which exact parameters generated it.

1. Experiment Tracking (MLflow / W&B)

Tools like MLflow and Weights & Biases automatically log the state of the code every time you hit "Train". They record: - Parameters: Learning Rate, Depth, Number of Trees, etc. - Metrics: Accuracy, F1-Score, Mean Squared Error, Training Time. - Artifacts: The actual trained model file (model.pkl), confusion matrix images, and sample datasets.

This allows a Data Scientist to open a web dashboard, sort the 500 experiments by "Highest Accuracy", and instantly download the winning .pkl file.

2. The Model Registry

A Model Registry is essentially "GitHub for Models". Once a model is trained and logged via MLflow, it is promoted to the Registry. The Registry controls the Lifecycle: - Version 1: Staging (Currently being tested by QA). - Version 2: Production (Currently serving live customer traffic). - Version 3: Archived (The old model replaced by V2).

When the backend engineering team needs to load the model into the API, their code simply asks MLflow for Production_Model, abstracting away the versioning chaos.

How to execute the examples:

Go to the Examples/ folder and run the script: python MLOps_MLflow_Tracking.py