Model Versioning Tracker

Overview

Track and manage AI/ML model versions using MLflow, DVC, or Weights & Biases. Log model metadata (hyperparameters, training data hash, framework version), record evaluation metrics (accuracy, F1, latency), manage model registry transitions (Staging, Production, Archived), and generate model cards documenting lineage and performance.

Prerequisites

• MLflow tracking server running locally or remotely (mlflow server or managed MLflow)
• Python 3.9+ with mlflow, pandas, and the relevant ML framework installed
• Model artifacts accessible on the local filesystem or cloud storage (S3, GCS)
• Write access to the MLflow tracking URI and artifact store

Instructions

1. Connect to the MLflow tracking server by setting MLFLOW_TRACKING_URI and verify connectivity with mlflow experiments list.
2. Create or select an MLflow experiment for the model project using mlflow experiments create --experiment-name <name>.
3. Log a new model version: start an MLflow run, log parameters (learning rate, epochs, batch size), log metrics (accuracy, loss, F1 score), and log the model artifact with mlflow.<flavor>.log_model().
4. Register the model in the MLflow Model Registry using mlflow.register_model() with the run URI and a descriptive model name.
5. Transition the model version through stages: None -> Staging -> Production using client.transition_model_version_stage(). Archive previous production versions.