📌 Key themes and curated posts
New here? These are some topics I write & speak about. Or navigate via tags or search.
Machine Learning Systems & Techniques
Exploring ML systems in industry and how they're implemented.
- Data Discovery Platforms: How they help with find data and open source options.
- Feature Stores: As a hierarchy of needs (e.g., access, serving, integrity, etc.)
- Real-time Recsys: Candidate retrieval, ranking, and how to design and build an MVP.
- Search Query Matching: Via lexical, graph, and representation learning methods.
- Patterns for Personalization: Via bandits, sequences, graphs, and user embeddings.
- 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.
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.
Interviews with Informal Mentors
Interviews with practitioners and dissecting their careers.
- Chip Huyen: From self-taught English to Stanford CS to Nvidia and beyond.
- Alexey Grigorev: From Java developer to lead data scientist at OLX Group.
Random philosophical ideas and thoughts.