MLOps

MLOps is an automated process managing, testing, deploying, and monitoring ML models production environment. Instead ML engineers just developing models jupyter notebooks, should be production available users with monitoring.

Problem MLOps solves: ML model just part system. Need: (1) Automatically train model with new data; (2) Test model if better than old; (3) Deploy new model without downtime; (4) Monitor if model works well production; (5) If model makes bad prediction, auto-revert.

Practical example: Netflix ML team trains new recommendation model new data daily. MLOps system: (1) Automatically download new data; (2) Automatically train new model; (3) Automatically test model test set; (4) If better, automatically deploy new model (usually 5-10% users); (5) Monitor performs—if worse than old, revert; (6) If all OK, deploy complete all users.

Key MLOps tools: (1) MLflow—tracking experiments; (2) Kubernetes—deployment; (3) Prometheus—monitoring; (4) Jenkins—CI/CD pipeline.

MLOps advantages: (1) Automation—everything automatic; (2) Fast development—ML teams iterate faster; (3) Quality—testing ensures model good; (4) Scalability—can scale millions users.

However, MLOps difficult: (1) Complexity—needs larger infrastructure; (2) Knowledge—needs knowledge DevOps and ML; (3) Costs—infrastructure expensive.

For startups: MLOps mandatory if need production ML systems.