Sources

These notes were compiled from the following sources. Examples and case studies come from them, not from the author’s own projects.

• Andrew Ng / DeepLearning.AI, Machine Learning Engineering for Production (MLOps) Specialization, now offered as the standalone course Machine Learning in Production

• Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (O’Reilly)

• Eugene Yan, Writing Docs: Why, What, and How

• Eugene Yan, What I Love About Scrum for Data Science

• Snoek, Larochelle & Adams, Practical Bayesian Optimization of Machine Learning Algorithms

• Martin Zinkevich, Rules of Machine Learning: Best Practices for ML Engineering (Google for Developers), licensed CC BY 4.0; rules are paraphrased and adapted, cited inline as (Rules of ML #n)

• AWS Cloud Operations Blog, Why you should develop a correction of error (COE), and the JDHarris007/coe example