Why Data Science Projects Need Follow-Up
A practitioner argues that revisiting data science projects rarely reproduces original results, making follow-up habits essential.
AI & ML
Frontier models, research results, agents, and developer tooling.
Eugene Yan's 2021 Roadmap: Sharing, Helping, Living More
The ML practitioner's public personal roadmap is explicitly non-technical, focusing on sharing, helping, and living more.
Eugene Yan Explains the Stack Behind His Blog
The post offers a practical, reproducible blueprint for setting up a technical blog.
Personalization Patterns: From Bandits to User Embeddings
Eugene Yan's tour of recommendation and search personalization maps five technique families and their trade-offs.
Bukalapak Fireside Chat: Building Data Teams and Business Ties
A fireside chat with Eugene Yan at Bukalapak focused on the practical challenges of running data teams and improving engagement with business stakeholders.
Coping with Impostor Syndrome in AI: A Personal Essay
A guest post on Eugene Yan's blog offers personal insights into managing impostor syndrome, a familiar struggle for high achievers in technical fields.
Design Docs for ML Systems: A Guide to Thinking Before Building
Eugene Yan outlines a practical approach to writing design documents that clarify methodology, implementation, and review for machine learning systems.
Winning Data Hackathons: It's Not About the ML Model
An analysis of top teams at Hacklytics 2021 shows that success came from data understanding, storytelling, and execution—not fancy algorithms.
Why Achievements Won't Cure Imposter Syndrome
Eugene Yan's essay argues that external validation fails to resolve imposter syndrome, and suggests alternative coping strategies.
Eugene Yan Explores Life Lessons from ML on Talk Python
The Talk Python episode shifts from technical talk to philosophy, using machine learning as a lens for life advice.
Eugene Yan on Seven Habits With Compounding Effects
In a personal retrospective, Eugene Yan says seven habits improved his life and several built on one another over time.
What Running a Data Science Team Taught Eugene Yan
A practitioner's guide to hiring, onboarding, and sustaining a healthy data science culture.
Choosing ML Problems: Weigh Short-Term Gains Against Long-Term Impact
Eugene Yan argues that data scientists should evaluate problem selection through tradeoffs between immediate returns, lasting impact, and career incentives.
Feature Stores: A Hierarchy of Needs, Not a Shopping List
A practical framework argues that teams should adopt only the feature-store capabilities that solve their actual ML bottlenecks.
Build an AI Career on Values and Superpowers
Eugene Yan's career framework gives AI/ML practitioners a way to filter opportunities by personal values and technical strengths.
Resume Advice for Experienced Data Scientists: Getting Recruiters' Attention
Eugene Yan's latest mailbag post tackles a common pain point: how experienced data scientists can improve their chances of being contacted by recruiters.
Behind the Scenes of a Healthcare ML System
Eugene Yan's talk breaks down the design, architecture, and lessons learned from building an ML system in healthcare.
From Engineer to Data Scientist: Alexey Grigorev on Career Growth and Writing
An interview with Alexey Grigorev reveals how switching roles, defining seniority, and writing publicly shaped his data science career.