> For the complete documentation index, see [llms.txt](https://lwang010.gitbook.io/longw/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://lwang010.gitbook.io/longw/mlops/preface.md).

# 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>[)](https://huyenchip.com/2020/10/27/ml-systems-design-stanford.html) which have a comprehensive topic related to the field.&#x20;
* 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.&#x20;

**How to use this book?**

Basically, the book presents a roadmap on what's expected from inception to the deployed product.&#x20;

* 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.&#x20;
* If you'd like to build some side hustle yourself, feel free to read thru from the beginning to the end.&#x20;

**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.&#x20;

**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.&#x20;

**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**](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.&#x20;

**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.&#x20;

**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/>&#x20;
* 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.