Evaluate your MLOps maturity

Operationalizing machine learning models has been a crucial stake for organizations which have invested in Artificial Intelligence. Indeed, many organizations launched PoCs (Proofs of Concepts) without succeeding in operationalizing their machine learning or deep learning models for different reasons: lack of expertise, or experience, reluctance of C-level executives to trust a new technology, no adapted processes or unwillingness of business to loose a part of their expertise or their understanding of decisions made by a model etc. To help to perform ML operationalization a new discipline appeared: MLOps for Machine Learning Operations. MLOps is part of the Ops family and is inspired from the DevOps concepts even though it has some specificities related to models management. This is the reason why we chose to evaluate the MLOps processes the same way DevOps processes are.