🔖 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.

• System Design for RecSys & Search: Offline vs. online, retrieval vs. ranking.
• Real-time Retrieval: Examples from various companies and how to build an MVP.
• Search Query Matching: Via lexical, graph, and representation learning methods.
• Patterns for Personalization: Via bandits, sequences, graphs, and user embeddings.
• Bandits for RecSys: Industry examples, warm-start, off-policy evaluation.
• Reinforcement Learning for Recsys: Long-term rewards and explore-exploit.
• Feature Stores: As a hierarchy of needs (e.g., access, serving, integrity, etc.)
• Data Discovery Platforms: How they help with find data and open source options.
• Bootstrapping Data Labels: With semi, active, and weakly supervised learning.

Machine Learning Techniques

Surveys on machine learning methods.

• Counterfactual Evaluation & Inverse Propensity Scoring: How to run A/B tests offline.
• Measuring and Mitigating Position Bias: Accounting for bias in ordered results.
• Measuring Serendipity in Recommendations: Diversity, novelty, unexpectedness, etc.
• A Brief Survey of NLP: From RNN to Word2Vec to Transformer to BERT to T5.

Machine Learning in Production

Thoughts and practices on putting ML into production.

• Challenges with ML in Production and A Practical Guide to Overcome them.
• ML Testing: Implementation, expected learned behavior, and evaluation metrics.
• Python Project Setup: With a workflow of checks that run with each  git push.

Data Science Mechanisms

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

• The First Rule of Machine Learning: Start without machine learning.
• End-to-End Data Science: Why I think there should be more end-to-end visibility, cohesion, and ownership, with examples from Stitchfix and Netflix.
• Scrum for Data Science: Parts of Scrum that fit the data science process well.
• Agile & Data Science: What works and what doesn’t and tweaks to make it work
• What I Do Before, During, and After a data science project for team productivity.

Writing

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

• Why, What, How: A simple framework to make your writing more effective.
• Design Docs for ML Systems: How to write the methodolgy and implementation sections of machine learning design docs, and the review process.
• Writing for Tech Careers: Why write, my writing process, and writing at work.
• Five Writing Lessons: Stuff I didn’t learn in school and my writing syllabus.

Learning & Career

Practices that worked well for me and general advice.

• Georgia Tech OMSCS FAQ: A guide to applying for and completing OMSCS.
• Comparing Data/ML Roles: Data/Applied/Research scientist, and ML engineer.
• Read Papers: How it helps you be a better data scientist and my approach.
• Influencing without Authority: Socratic method, earning trust, finding advocates, etc.
• Data Science Red Flags: What to look out for when interviewing with a new team.
• Onboarding Effectively: Mindset, 100-day plan, and balancing learning and doing.

Ideas

Random philosophical ideas and thoughts.

• The 85% Rule: When pushing harder != better outcomes
• Beginner’s Mind: A mindset for continued learning and growth.
• My Seven Habits: The seven habits that improved my last decade.
• Machine Learning & Life Lessons: Seven things ML taught me about life.

Summaries & Notes

Summaries and permanent notes, tidied up for public consumption.

• RecSys 2020: Bias, sequential models, bandits, robust offline evaluation.
• RecSys 2021: Simple models, tech stacks, and ops practices.
• Spark+AI Summit 2020: Application-agnostic and application specific talks.

Join 3,200+ readers getting updates on data science, ML systems, & career.

Get email updates

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