MLOps vs DevOps: A Practical Guide for Data Scientists and IT Teams

MLOps extends DevOps to machine learning by managing code, data, and models with Continuous Training to handle model decay.

• •MLOps adds data versioning, model artifact tracking via MLflow/DVC, and governance beyond traditional code-only DevOps
• •Model drift causes models to degrade in accuracy over time despite unchanged source code, requiring dedicated monitoring
• •MLOps CI/CD includes data validation, feature engineering, training validation, and gating rules before production
• •Teams must collaborate to manage GPU-intensive training and ensure reproducibility with versioned data and locked dependencies

This summary was automatically generated by AI based on the original article and may not be fully accurate.