MLOps
A set of practices for deploying, monitoring, and maintaining machine learning models in production
What is MLOps?
MLOps, short for Machine Learning Operations, is the set of practices and tools used to reliably deploy, monitor, and maintain machine learning models in real-world production environments. Think of it like the difference between cooking a great meal once in your kitchen versus running a restaurant. The recipe (model) is only part of the equation. You also need supply chains (data pipelines), quality control (monitoring), consistent plating (deployment), and the ability to update the menu smoothly (retraining).
How Does It Work?
MLOps brings together principles from software engineering (DevOps) and data science into a unified workflow. A typical MLOps pipeline includes several stages: data collection and validation, model training and evaluation, packaging and deployment, and ongoing monitoring. Tools like MLflow track experiments and model versions. Container technologies like Docker ensure models run consistently across environments. CI/CD pipelines automate testing and deployment so that updated models can be rolled out safely. Monitoring systems watch for data drift, where incoming real-world data starts to differ from training data, signaling that a model may need retraining.
Why Does It Matter?
Studies show that most machine learning projects never make it to production, and of those that do, many degrade over time without proper maintenance. MLOps addresses this gap by providing structure and automation around the entire model lifecycle. For businesses, it means faster time to value, more reliable AI systems, and the ability to scale from one model to hundreds without chaos. As AI becomes embedded in critical applications, robust MLOps practices are no longer optional but essential.