A machine learning model is never 'finished.' It is a living component of a system that must evolve as the data it processes evolves.
1Data Ingestion & Tracking
The foundation of the lifecycle is Reproducibility. Every training run must be traceable back to the exact code version and dataset version used. We use DVC to manage large datasets in Git and Experiment Tracking tools like MLflow to record every mathematical decision. If a model fails in production, we must be able to recreate its exact training environment to debug the failure.
# The ML Lifecycle Loop
# 1. Ingestion
# 2. Training
# 3. Tracking
# 4. Deployment
# 5. Monitoring2Deployment & Serving
Transitioning from a saved model file (like a .pkl) to a live API is Serving. This stage involves containerizing the model using Docker to ensure it runs consistently across dev and production environments. We then expose the model through high-performance web frameworks like FastAPI, allowing other services in our architecture to receive predictions in milliseconds.
$ dvc add data/raw_data.csv
$ git add data/raw_data.csv.dvc
$ git commit -m "Version 1.0 dataset"3Monitoring & Retraining
Once 'live,' a model enters the Monitoring phase. Unlike traditional code, a model can fail 'silently'—it still returns numbers, but those numbers are no longer accurate because the real world has shifted. MLOps systems monitor these statistical shifts and automatically trigger Continuous Training (CT) pipelines to update the model on fresh data, closing the lifecycle loop.
with mlflow.start_run():
mlflow.log_param("lr", 0.01)
mlflow.log_metric("accuracy", 0.95)
mlflow.sklearn.log_model(model, "classifier")