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 lwang010@gmail.com 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 (lwang010@gmail.com) first.
Could you point me to other similar resources as well?
Here are a few I find great and highly recommend:
MLops tooling landscape: https://huyenchip.com/2020/12/30/mlops-v2.html
Full-stack deep learning (UC Berkeley): https://fullstackdeeplearning.com/
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/
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.
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