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MLOps Tracking (MLflow & DVC)

🚀 MLOps Tracking (MLflow & DVC)

MLOps is the practice of applying DevOps principles to Machine Learning. This phase focuses on experiment tracking and data versioning.

🏗️ 1. Why Track Experiments?

  • Reproducibility: Can you re-run the same experiment with the same results?
  • Comparison: Which hyperparameters produced the best model?
  • Governance: Who trained this model, and what data was used?

🚀 2. MLflow: The Standard for Tracking

MLflow allows you to log parameters, metrics, and models.

import mlflow

# Start a tracking run
with mlflow.start_run():
    mlflow.log_param("learning_rate", 0.01)
    mlflow.log_metric("accuracy", 0.95)
    
    # Log the model artifact
    mlflow.sklearn.log_model(model, "model")

📦 3. DVC: Data Version Control

Git is terrible at versioning large datasets. DVC handles this by versioning small metadata files in Git while storing the actual data in a remote (S3, GCS, Azure Blob).

DVC Workflow:

  1. dvc add data.csv (Adds metadata to Git).
  2. dvc push (Pushes actual data to S3).
  3. git commit data.csv.dvc (Commits the pointer to Git).
  4. dvc pull (Anyone can download the specific version of the data).

🚦 4. Model Registry & Deployment

Once a model is trained, it’s moved to a Model Registry. This allows you to:

  • Tag models as Staging, Production, or Archived.
  • Version your model artifacts.
  • Automatically trigger deployment pipelines when a model is tagged as Production.