Totally, open-source, and made by the same development team that created the popular DVC (data version control) library, CML (Continous Machine Learning) library is a great tool that can be used to automate Machine learning workflows, including model training and evaluation, comparing ML experiments across your project history, and monitoring changing datasets. This brings the power of DevOps to ML or MLOps.
CML is built with the objective of bringing ML projects, and MLOps practices in a way such that it should be built on top of traditional engineering tools and not as a separate stack. This could be the future of MLOps.
CML tool is built with the following principles:
- GitFlow for data science. Use GitLab or GitHub to manage machine learning ML experiments, track who trained ML models or modified data, and when. Codify data and models with DVC instead of pushing to a Git repo.
- Automated reports for ML experiments. Auto-generate reports with analytics in each Git Pull Request.
- No additional services. Build your own ML platform using just GitHub or GitLab and your favorite cloud services: AWS, Azure, GCP. No databases, services, or complex setup needed.
Use Cases: https://cml.dev/#use-cases