This article was originally published on AI Study Room . For the full version with working code examples and related articles, visit the original post. MLOps Pipeline: From Training to Production MLOps applies DevOps principles to machine learning. A robust MLOps pipeline automates the ML lifecycle from data preparation through production monitoring, ensuring reliable and reproducible model deployments. Pipeline Stages An MLOps pipeline includes: data ingestion (collect raw data), data validation (check schema, statistics, anomalies), feature engineering (transform raw data), model training (train with hyperparameter tuning), model evaluation (validate against test sets), model deployment (promote to production), and monitoring (track performance in production). Each stage produces artifacts that the next stage consumes. Artifact versioning enables reproducibility. Pipeline orchestration (Kubeflow, MLflow, Airflow) manages stage execution, retries, and failure handling.…