How to: MLOps Core
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1. [Machine Learning Operations: You Design It, You Train It, You Run It!](https://ml-ops.org/)
1. [MLOps SIG Specification](https://github.com/tdcox/mlops-roadmap/blob/master/MLOpsRoadmap2020.md)
1. [ML in Production](http://mlinproduction.com/)
1. [Awesome production machine learning: State of MLOps Tools and Frameworks](https://github.com/EthicalML/awesome-production-machine-learning)
1. [Udemy “Deployment of ML Models”](https://www.udemy.com/course/deployment-of-machine-learning-models/)
1. [Full Stack Deep Learning](https://course.fullstackdeeplearning.com/)
1. [Engineering best practices for Machine Learning](https://se-ml.github.io/practices/)
1. [:rocket: Putting ML in Production](https://madewithml.com/courses/putting-ml-in-production/)
1. [Stanford MLSys Seminar Series](https://mlsys.stanford.edu/)
1. [IBM ML Operationalization Starter Kit](https://github.com/ibm-cloud-architecture/refarch-ml-ops)
1. [Productize ML. A self-study guide for Developers and Product Managers building Machine Learning products.](https://productizeml.gitbook.io/productize-ml/)
1. [MLOps (Machine Learning Operations) Fundamentals on GCP](https://www.coursera.org/learn/mlops-fundamentals)
1. [ML full Stack preparation](https://www.confetti.ai/)
1. [MLOps Guide: Theory and Implementation](https://mlops-guide.github.io/)
1. [Practitioners guide to MLOps: A framework for continuous delivery and automation of machine learning.](https://services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdf)
1. [MLOps maturity assessment](https://github.com/marvelousmlops/mlops_maturity_assessment)