https://www.datacamp.com/courses/mlops-concepts
https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
https://www.xomnia.com/post/how-to-standardize-and-streamline-machine-learning-life-cycles/
https://towardsdatascience.com/mlops-in-practice-de-constructing-an-ml-solution-architecture-into-10-components-c55c88d8fc7a
https://github.com/benvliet/eneco/blob/main/mlops.md
https://medium.com/picnic-engineering/mlops-principles-to-build-picnics-data-science-platform-851cbe2e8045
https://www.oreilly.com/library/view/practical-mlops/9781098103002/
https://github.com/noahgift/Python-MLOps-Cookbook
https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops#howItWorks
https://realpython.com/learning-paths/python-devops/
https://realpython.com/courses/how-to-publish-your-own-python-package-pypi/
https://developers.google.com/machine-learning
https://developers.google.com/machine-learning/guides/rules-of-ml