MLOps Maturity Assessment – Making maturity transparent, planning improvements

Successful machine learning (ML) systems need more than powerful models; they require mature processes that seamlessly connect development, deployment and operations. Fraunhofer FOKUS’ MLOps Maturity Assessment provides a hands-on framework for exactly this purpose. Using a five-level maturity scheme, organisations can evaluate their current MLOps set-up, pinpoint strengths and weaknesses, and define targeted next steps.

The assessment covers seven key domains, including data gathering & preparation, model training, deployment and monitoring. Each domain comes with well-defined maturity statements that are scored in an Excel-based self-assessment. Results are consolidated and visualised, offering an at-a-glance overview that sparks discussion across business, data-science and IT teams, reveals collaboration gaps and lays the foundation for a prioritised roadmap towards efficient, scalable MLOps.

With the MLOps Maturity Assessment, companies gain a concise tool to benchmark their ML lifecycle, compare it with industry best practices and systematically unlock business value from machine learning.