MLOps is the practice of deploying, monitoring and updating machine learning models in production. It applies DevOps discipline to models and the data they depend on, so releases are repeatable, auditable and reversible.
DevOps automates building, testing and running code, while MLOps extends that practice to the data, training and evaluation that machine learning models need. MLOps also tracks model drift after release, a problem ordinary application code does not face.
Model drift is the decline in a machine learning model's accuracy over time. It happens when the data or real-world patterns seen in production move away from those it was trained on.
An AI engineer builds products on top of existing models, such as large language models, while an ML engineer trains, tunes and serves models from data. Which to hire depends on whether you need to apply models or create them.
DataOps is a set of practices that applies DevOps and agile ideas to data work. Automation, testing, version control and monitoring help teams deliver data products quickly and reliably.
An evaluation set is a fixed collection of inputs with expected outcomes, used to score an AI system the same way every time it changes. It plays the role a regression test suite plays for ordinary code.
A feature store is a system that stores, documents and serves the input features used by machine learning models. It keeps them consistent between training and live predictions.
A clear proven process, that delivers
You've seen our ideas - now go deeper.
Discover more insights, guides, and engineering practice.