MLOps

MLOps (Machine Learning Operations) refers to the set of practices, tools, and processes for deploying, monitoring, maintaining, and continuously improving machine learning models in production environments. It's the operational side of machine learning, analogous to DevOps for software development. MLOps encompasses several key areas. Model development involves creating and training models, often with version control and experiment tracking. Model deployment involves packaging models and deploying them to production environments where they serve real users. Model monitoring tracks model performance, data quality, and system health in production. Model maintenance includes retraining models when performance degrades, updating models with new data, and managing model versions. Key MLOps practices include version control for both code and models, automated testing of models, continuous integration and deployment (CI/CD) pipelines for models, monitoring and alerting systems, and documentation. Tools in the MLOps ecosystem include experiment tracking platforms (MLflow, Weights & Biases), model registries, containerization (Docker), orchestration (Kubernetes), and monitoring solutions. MLOps is essential because deploying models to production is fundamentally different from developing them in notebooks. Production models must be reliable, scalable, maintainable, and continuously monitored. Without proper MLOps practices, models can fail silently, degrade in performance, or cause unexpected problems. As organizations increasingly rely on machine learning, MLOps has become a critical discipline. It bridges the gap between data scientists who build models and engineers who operate systems, ensuring models deliver value reliably and sustainably. Investing in MLOps practices early prevents costly problems and enables organizations to scale machine learning effectively.