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Once an organization has the ability to quickly operationalize data projects and moves from a handful to hundreds (or thousands) of machine learning models in production, the question of maintenance and management arise. Enter: MLOps.
“Technology innovation leaders are keen to apply DevOps principles for AI and ML projects, but they often struggle with architecting a solution for automating end-to-end ML pipelines across data preparation, model building, deployment and production due to lack of process and tooling know-how.”
– Gartner, Accelerate Your Machine Learning and Artificial Intelligence Journey Using These DevOps Best Practices, 12 November 2019, Arun Chandrasekaran and Farhan Choudhary