Short guide to deploy Machine Learning

While this article is a nice read, and provides what may be useful tips, "guide to deploy" feels misleading. I thought it was going to be a guide on deploying ML to production.

Simple

1. pip install scikit-learn

2. call model inside flask request

3. docker build && docker push

Joking aside, following articles are good: http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf https://research.google.com/pubs/pub43146.html

Heads up : Article is not talking about a practical production deployment of ML as the title suggests .

Where would I find such an article, something that explains how I might go from "I've got a Jupyter notebook that gives me results I like" to 'I'm running those calculations in a production system'?

Take a look at Google's "Best Practices for ML Engineering" [1] and "Machine Learning: The High Interest Credit Card of Technical Debt" [2]. They are not tutorials, but cover a number of interesting topics related to deployment of machine learning models to production.

[1] https://news.ycombinator.com/item?id=13414776

[2] https://news.ycombinator.com/item?id=10338575

Thanks, I'll dig into those

Is it just me or is this link pointing a rehost of the original rather than the original. I hope that's okay for the author...