As I thought about conducting more interviews, I realized the stories, advice, and insight should be on a site on their own. Thus, ApplyingML.com was created.

Here’s what ApplyingML.com is about:

Applying machine learning is hard. Many organizations have yet to benefit from ML, and most teams still find it tricky to apply it effectively.

Though there are many ML courses, most focus on theory and students finish without knowing how to apply ML. Practical know-how is gained via hands-on experience and seldom documented—it’s hard to find it in a textbook, class, or tutorial. There’s a gap between knowing ML vs. applying it at work.

To fill this gap, ApplyingML collects tacit/tribal/ghost knowledge on applying ML via curated papers/blogs, guides, and interviews with ML practitioners. In a nutshell, it’s 1/3 applied-ml, 1/3 ghost knowledge, and 1/3 Tim Ferriss Show. The intent is to make it easier to apply—and benefit from—ML at work.

Want to contribute? Please make a pull request at the github repo.

If you found this useful, please cite this write-up as:

Yan, Ziyou. (Nov 2021). Informal Mentors Grew into ApplyingML.com!. eugeneyan.com. https://eugeneyan.com/writing/applyingml/.

or

@article{yan2021applying,  title   = {Informal Mentors Grew into ApplyingML.com!},  author  = {Yan, Ziyou},  journal = {eugeneyan.com},  year    = {2021},  month   = {Nov},  url     = {https://eugeneyan.com/writing/applyingml/}}