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.
- 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.
- Content Moderation: Collecting labels, data augmentation, cascade pattern, etc.
- Bootstrapping Data Labels: With semi, active, and weakly supervised learning.
- 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.)
Machine Learning Techniques
Surveys on machine learning methods.
Machine Learning & Engineering
Practices at the intersection of ML and engineering.
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.
Especially in the context of a career in tech and data.
Ideas & Opinions
Random ideas and unnecessarily strong opinions.
Best Talks I’ve Given
Talks that've received the most positive feedback and engagement.
Summaries & Notes
Summaries and permanent notes, tidied up for public consumption.
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.
- papermill-mlflow: Experimentation workflow for machine learning.
- 1-on-1s: Questions to ask during 1-on-1s, from my time as a manager.