🔖 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.
- 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 in Production
Thoughts and practices on putting ML into production.
Data Science Methodology
Thoughts on what an effective data science process should look like.
Especially in the context of a career in tech and data.
Learning & Career
Practices that worked well for me and general advice.
Random philosophical ideas and thoughts.
Summaries & Notes
Summaries and permanent notes, tidied up for public consumption.