Key themes and curated posts

New here? These are some topics I write & speak about. Or navigate via tags or search.

Machine Learning Systems

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.

Mechanisms for ML and Data Science

Thoughts on what an effective data science process should look like.

Ideas & Opinions

Random ideas and unnecessarily strong opinions.


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

Learning & Career

Practices that worked well for me and general advice.

Summaries & Notes

Summaries and permanent notes, tidied up for public consumption.

Other resources

That are mostly scattered across the internet.

  • applied-ml: Papers and tech blogs on real-world machine learning in industry.
  • ml-surveys: Papers summarizing machine learning advances.
  • applyingml: Papers, guides, and interviews on how to apply ML effectively.
  • 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.
  • recsys-nlp-graph: Simple recsys and experiment results (built on PyTorch).
  • papermill-mlflow: Experimentation workflow for machine learning.

Join 4,000+ readers getting updates on data science, ML systems, & career.

    Welcome gift: A 5-day email course on How to be an Effective Data Scientist 🚀