Machine learning operations

An ML workflow is a set of operations developed and executed to produce a mathematical model, which eventually is designed to solve a real-world problem. But there is no value of these models until they are deployed in production, other than proofs of concept. ML models almost always require deployment to a production environment to provide business value.

At the core, Machine Learning Operations (MLOps) takes an experimental ML model into a production system. MLOps is an emerging practice different from traditional DevOps because the ML development lifecycle and ML artifacts are different. The ML lifecycle involves using patterns from training data, making the MLOps workflow sensitive to data changes, volumes, and quality. Additionally, matured MLOps should support monitoring both ML lifecycle activities and production model monitoring.

MLOps framework implementation makes it simple for organizations to feel confident in building a mature MLOps...