An empirical guide to MLOps adoption: Framework, maturity model and taxonomy

Context:

Machine Learning Operations (MLOps) has become a top priority for companies. However, its adoption has become challenging due to the need for proper guidance and awareness. Most of the MLOps solutions available in the market are designed to fit the specific platform, tools and culture of the providers.

Objective:

The objective is to develop a structured approach to adopting, assessing and advancing MLOps adoption.

Methods:

The study was conducted based on a multi-case study across fourteen companies.

Results:

We provide a comprehensive analysis that highlights the similarities and differences in the adoption of MLOps practices among companies. We have also empirically validated the developed MLOps framework and MLOps maturity model. Furthermore, we carefully reviewed the feedback received from practitioners and revised the MLOps framework and maturity model to confirm its effectiveness. Additionally, we develop an MLOps taxonomy for classifying ML use cases based on their context and requirements into the desired stage of the MLOps framework and maturity model.

Conclusion:

The findings provide companies with a structured approach to adopt, assess, and further advance the adoption of MLOps practices regardless of their current status.