What Is MLOps?
MLOps (Machine Learning Operations) is a set of practices that combines machine learning, DevOps, and data engineering to automate and streamline the entire lifecycle of ML models — from development and training to deployment, monitoring, and retraining in production.
How MLOps Works
Building an AI model in a notebook is only the beginning; deploying and maintaining it in production is where the real challenges lie. MLOps provides the frameworks, tools, and best practices for managing this lifecycle. It encompasses version control for data and models, automated training pipelines, continuous integration and deployment for ML, model monitoring for drift and degradation, A/B testing of model versions, and automated retraining when performance drops. Without MLOps, organizations struggle with reproducibility issues, slow deployment cycles, and models that degrade silently in production. MLOps has become essential as companies scale from experimental AI projects to production AI systems.
Real-World Examples
A team using Weights & Biases to track experiments, then deploying the best model version through an automated CI/CD pipeline
An MLOps platform automatically retraining a fraud detection model weekly as new transaction patterns emerge
A company using model monitoring to detect that their recommendation model's accuracy has dropped 5% and triggering a retraining job