Chap 0. Before everything

Some FAQ I assume you'd like to know before moving forward.

Am I the right audience for the book?

If the keyword you're looking for is Machine Learning OPS. YES!!

The note here is my collections/takeaways of technical notes from 0 to a deployed model to a launched product.

• If you are new and interested in the MLops or full-stack ML, you can start from https://course.fullstackdeeplearning.com/ or take the course `Machine Learning Systems Design`(https://huyenchip.com/2020/10/27/ml-systems-design-stanford.html) which have a comprehensive topic related to the field.

• If you are practicing MLops, some notes in this notebook would be helpful for you as another tutorial, guide some directions for issues resolving, and provide some good practice as a reference that could be adapted in your setup.

How to use this book?

Basically, the book presents a roadmap on what's expected from inception to the deployed product.

• If you are curious or have specific questions on one topic, go to the chapter that's relevant directly, or shot an email to [e-mail] if you didn't find the information.

• If you'd like to build some side hustle yourself, feel free to read thru from the beginning to the end.

Could I report issues? How shall I do that?

Any feedback is always welcomed. Please use this form(https://forms.gle/5K9GsurxUVakBWNS8) to submit issues. I'll get back to you as soon as possible. Also, your name will be added to acknowledge and co-author if the feedback is valid and helpful.

Why do you write the book?

I got much help and great advice, and have the opportunities to work with great people during the journey, which made me wonder what I can do to return the favor. Hopefully, working on this is one way to pass the torch and return to the community.

Where can I find the source code?

Most of the source code is still under construction. Some source code can be found at https://github.com/longw010/gitbook_suppl. If it is not available here, please use the error report link. I'll update the repository and keep you informed.

Are the materials here open-source?

All the contents and code here are available online for free but are copyrighted and licensed under the MIT license. If you'd like to use it, please do contact me ([e-mail]) first.

Could you point me to other similar resources as well?

Here are a few I find great and highly recommend:

• https://madewithml.com/courses/applied-ml-in-production/
• MLops tooling landscape: https://huyenchip.com/2020/12/30/mlops-v2.html
• https://pythonspeed.com/
• Full-stack deep learning (UC Berkeley): https://fullstackdeeplearning.com/
• Best practice of ML engineering: https://media-exp1.licdn.com/dms/document/C4E1FAQEQN6LklNVocA/feedshare-document-pdf-analyzed/0/1613362485790?e=1613538000&v=beta&t=OMnih5uD7fznCUExOPiE3SHb9dNRn-UkU177oUTACLk
• Research Software Engineering with Python: https://merely-useful.github.io/py-rse/
• ML in production: https://mlinproduction.com/why-i-started-mlinproduction/
• Machine Learning Systems Design: https://huyenchip.com/machine-learning-systems-design/toc.html
• Deep learning in Production: https://www.reddit.com/r/MachineLearning/comments/m0ew90/d_deep_learning_in_production/
• https://neptune.ai/blog/learn-mlops-books-articles-podcasts
• https://mlops.githubapp.com/

Is there a way for me to subscript to this site?

YES!! Filled in the form here(https://forms.gle/wQu2GtBAJtqKYmSv7) and you'll be notified with all the major updates for this work.