Eugene Yan is a Principal Applied Scientist at Amazon, building AI-powered recommendation systems and experiences. He shares insights on RecSys, LLMs, and applied machine learning, while mentoring and investing in ML startups.
188 articles from this blog
A technical deep dive into real-time machine learning for recommendation systems, comparing approaches in China and the US and discussing implementation.
A data scientist's 2020 reflection on moving to Amazon, building ML systems, and establishing a weekly writing habit for learning and sharing knowledge.
An interview with lead data scientist Alexey Grigorev on his career transition from software engineering to data science, his advice, and his work at OLX.
The author explains the simple, static architecture of their blog, built with Jekyll, GitHub Pages, HTML, CSS, and minimal JavaScript.
Advice on avoiding or handling data science job mismatches by scrutinizing job descriptions, asking key interview questions, and researching team culture.
Explains the differences between Applied Scientist, Research Scientist, and ML Engineer roles in data science and machine learning.
An interview with Chip Huyen about her journey from a small village to Stanford and a career in ML, her writing, and thoughts on machine learning in production.
An analysis of data discovery platforms, their key features, and available open-source solutions to improve data findability in organizations.
A developer explains why they switched their site hosting back to GitHub Pages from Netlify due to DNS issues, certificate problems, and build time limits.
Explores the intrinsic motivations for building a data science portfolio beyond just getting a job, covering learning, helping others, and enjoyment.
A step-by-step guide to installing Google's ScaNN library for efficient vector similarity search on macOS, covering dependencies and troubleshooting.
Explains how building a simple prototype can be more effective than proposals for gaining stakeholder buy-in on tech projects.
Explores the growing importance of writing vs. coding for senior tech roles, featuring insights from engineers and data scientists on communication and leadership.
Key takeaways from RecSys 2020 conference, focusing on ethics, bias, sequence models, and notable papers in recommender systems.
An interview with an Amazon Applied Scientist describing the daily work, challenges, and projects involved in building ML systems like book recommendations.
A developer shares his personal productivity system, tools, and routines for balancing a full-time job, a Master's in CS, and side projects.
A technical guide on migrating website comments from Commento to Utterances, a GitHub-based comment system, including steps and code.
A guide to testing machine learning code and systems, covering pre-train and post-train tests, evaluation, and implementation with a DecisionTree example.
A developer asks when to use ML for parsing PDF fields with typos, and receives advice on using Levenshtein distance and human-in-the-loop solutions.
A podcast interview with data scientist Eugene Yan discussing his career transition, data science leadership, and experiences at Lazada.
