Machine Learning Operations (MLOps): Overview, Definition, and Architecture

The final goal of all industrial machine learning (ML) projects is to develop ML products and rapidly bring them into production. Additionally, we provide a comprehensive definition of MLOps and highlight open challenges in the field.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 However, it is highly challenging to automate and operationalize ML products and thus many ML endeavors fail to deliver on their expectations.
• 2 The paradigm of Machine Learning Operations (MLOps) addresses this issue.
• 3 MLOps includes several aspects, such as best practices, sets of concepts, and development culture.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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