Building Production-Grade MLOps Pipelines from Scratch

The gap between a working Jupyter notebook and a production ML system is enormous. MLOps — the practice of applying DevOps principles to machine learning — bridges that gap.

The MLOps Stack

A production MLOps pipeline includes: data versioning (DVC), experiment tracking (MLflow), model registry, automated training pipelines, model serving infrastructure, and monitoring for data drift and model degradation.

Data Pipeline Architecture

Your data pipeline is only as good as your data quality. Implement automated data validation, schema enforcement, and lineage tracking. Tools like Great Expectations can catch data quality issues before they corrupt your models.

Model Serving Patterns

For real-time inference, deploy models behind a FastAPI service with horizontal autoscaling. For batch predictions, use orchestrated pipelines (Airflow, Prefect) with scheduled runs and result caching.

Monitoring and Retraining

Models degrade over time as data distributions shift. Implement automated monitoring for prediction accuracy, input data drift, and concept drift. Trigger retraining pipelines when performance falls below defined thresholds.