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