In The Checklist Manifesto, Atul Gawande shows how using checklists can make everyone's work more efficient and less error-prone. If they are useful for aircraft pilots and surgeons, we could use them to help us with deploying machine learning models as well. While most of those steps might sound obvious, it's easy to forget them, or leave them for "somebody" to do "later". In many cases, skipping those steps will sooner or later lead to problems, hence it's good to have them as a checklist. For more details, you can check resources like Introducing MLOps book by Mark Treveil et al, Building Machine Learning Powered Applications book by Emmanuel Ameisen, the free Full Stack Deep Learning course, Rules of Machine Learning document by Martin Zinkevich, ML Ops: Operationalizing Data Science report by David Sweenor et al, the Responsible Machine Learning report by Patrick Hall et al, the Continuous Delivery for Machine Learning article by Danilo Sato et al, Machine Learning Systems Design page by Chip Huyen, and the ml-ops.org