Key themes in my work

New here? These are topics I write & speak about. Alternatively, browse tags or search.

Machine Learning Systems in Industry

Exploring ML systems in industry and how they're implemented.

Machine Learning Techniques

Surveys on machine learning methods.

Machine Learning & Engineering

Practices at the intersection of ML and engineering.

Weekend Prototypes

I don't get to hack as much as I want, but when I do, they're a ton of fun.

Mechanisms for Business, Product, and Tech Teams

Processes and tools for effective projects and teams.

Learning & Career

Practices that worked well for me and general advice.

Writing Tips

Especially in the context of a career in tech and data.

Talks that've received the most positive feedback and engagement.

Ideas & Opinions

Random ideas and unnecessarily strong opinions.

Summaries & Notes

Summaries and permanent notes, tidied up for public consumption.

  • LLM Reading List: Fundamental papers and their one-sentence summaries.
  • RecSys 2022: 3 fav papers, 17 summaries, and many lessons.
  • RecSys 2021: Simple models, tech stacks, and ops practices.
  • RecSys 2020: Bias, sequential models, bandits, robust offline evaluation.

Other resources

That are mostly scattered across the internet.

  • Practical lessons from a year of building with LLMs.
  • applied-ml: Papers and tech blogs on real-world machine learning in industry.
  • Papers, guides, and interviews on how to apply ML effectively.
  • open-llms: Open LLMs available for commercial use.
  • ml-design-docs: Template of design docs for machine learning systems.
  • testing-ml: Examples of implementation & behavioral tests for ML code.
  • python-collab-template: Template with tests, type checks, linting, etc.
  • papermill-mlflow: Experimentation workflow for machine learning.
  • 1-on-1s: Questions to ask during 1-on-1s, from my time as a manager.

185 posts, 27 talks, 14 prototypes, 358,920 words, and countless hours.

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