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.
185 articles from this blog
An interview with data scientist Eugene Yan discussing his career path from psychology to Amazon, favorite ML projects, and advice for aspiring data scientists.
Explores the strategic 'metagame' of applying machine learning in industry, focusing on problem selection and business impact over pure technical knowledge.
Explores three core methods—lexical, graph, and embedding-based—for matching user queries to documents in search systems.
A guest post sharing personal stories of imposter syndrome in tech and academia, with lessons on recognizing and managing self-doubt.
A guide to planning your tech career by identifying your core values and unique strengths to make fulfilling long-term choices.
A data science leader shares insights from a fireside chat on building and running data teams, focusing on their role as profit centers and collaboration strategies.
A podcast episode exploring life lessons derived from machine learning concepts like data cleaning, explore-exploit, and overfitting.
A guide for data science leaders on how to strategically select the most impactful problems for a team to work on, using frameworks like cost-benefit analysis.
A guide on writing effective design documents for machine learning systems, covering structure, purpose, and a two-stage review process.
A data scientist explains the 'Why, What, How' framework for writing effective technical documents like one-pagers, design docs, and after-action reviews.
Explores the concept of feature stores in machine learning, presenting a hierarchy of needs from basic access to full automation.
A data scientist shares key strategies for winning a data hackathon, based on judging Hacklytics 2021, covering evaluation criteria and time-saving tips.
A behind-the-scenes look at designing and implementing a production machine learning system for a major hospital group, covering architecture and validation.
A data science leader shares insights on hiring, training, and managing effective data science teams, based on experience at Lazada and uCare.
Argues that taking more MOOCs has diminishing returns for tech professionals and advocates for hands-on, project-based learning instead.
A podcast transcript discussing the importance of writing for career growth in tech, covering motivation, process, and Amazon's writing culture.
Advice for experienced data scientists on optimizing their resumes to attract recruiter attention, covering side projects, Kaggle, and academic work.
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.
