Awesome
MLOps maturity assessment
As more and more companies rely on machine learning to run their daily operations, it’s becoming important to adopt MLOps best practices. However, it can be hard to find structured information on what those best practices actually are and how a company can become more mature in terms of MLOps if they decide to start on that journey.
Our questionnaire covers a wide range of MLOps aspects, making it a valuable resource for teams looking to improve their MLOps practices.
- Documentation
- Traceability & Reproducibility
- Code quality
- Monitoring & Support
- Data transformation pipelines & Feature store
- Model explainability
- A/B testing & Feedback loop
We believe that a machine learning project can be considered MLOps mature if all statements in sections 1–4 (documentation, traceability & reproducibility, code quality, monitoring & support) can be answered with “yes”. This is the bare minimum required to deploy a machine learning project reliably in production. Sections 5–7 go beyond basic MLOps maturity, and it is possible to work on its implementation while some of the statements in sections 1–4 haven’t been covered. However, we encourage everyone to prioritize the basics before moving on to advanced MLOps practices. This questionnaire makes MLOps actionable by clearly highlighting areas for improvement: If some of the statements are answered with a “no,” it’s a clear indicator of what needs to be addressed to improve your MLOps maturity.
For more details, see our blog: https://medium.com/marvelous-mlops/mlops-maturity-assessment-7e6df189077b