• Papers
  • Resources
  • Mentors

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.

Read some of the guides below, or browse all guides.

  • Data Scientists should be More End-to-end
  • Feature Stores - A Hierarchy of Needs
  • How to Write Machine Learning Design Documents
  • System Design for Discovery (RecSys and Search)
  • Bootstrapping Labels via ___ Supervision & Human-In-The-Loop

  • Or read some mentor interviews, or browse all interviews.

  • Jeremy Jordan - Machine Learning Engineer @ Duo Security
  • Adam Laiacano - Staff Engineer (ML Platform) @ Spotify
  • Benjamin Wilson - Principal Solutions Architect @ Databricks
  • Hamel Husain - Staff Machine Learning Engineer @ GitHub
  • Vicki Boykis - Machine Learning Engineer @ Tumblr
  • Want to contribute an interview? Please reach out!

    © Eugene Yan 2021AboutSuggest edits.