Eugene Yanayt
PulseAugur coverage of Eugene Yanayt — every cluster mentioning Eugene Yanayt across labs, papers, and developer communities, ranked by signal.
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Eugene Yan distinguishes high-level intent from low-level requirements in product development
Eugene Yan's article distinguishes between high-level intent and low-level requirements in project execution. High-level intent focuses on the 'why' and 'what' with a broad perspective, akin to understanding the forest.…
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Data scientists urged to prioritize business goals over model training
Eugene Yan's article offers guidance for data scientists on initiating projects effectively by prioritizing business goals and context over immediate model training. He emphasizes understanding the project's intent, def…
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Eugene Yan offers guidance on data team vision and roadmap creation
Eugene Yan, a writer on data leadership, responded to a reader named E who sought guidance on establishing a vision and roadmap for a new data team. Yan suggested a process involving stakeholder interviews to identify k…
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Data pros: Avoid teams lacking infra, clear roadmaps, and defined roles
Eugene Yan's article highlights critical red flags for individuals seeking roles in data science teams. Key concerns include the absence of robust data infrastructure, a poorly defined roadmap for delivering business va…
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Eugene Yan shares strategies for continuous machine learning education
Eugene Yan's essay offers practical advice for staying current in the rapidly evolving field of machine learning. He suggests actively experimenting with new tools and techniques in projects, sharing learnings with coll…
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Eugene Yan launches ApplyingML.com to bridge the gap in practical machine learning knowledge
Eugene Yan has launched ApplyingML.com, a platform dedicated to bridging the gap between theoretical machine learning knowledge and its practical application in the workplace. The site aims to collect and share "ghost k…
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Eugene Yan shares 5 lessons learned from writing online for aspiring creators
Eugene Yan shares five key lessons learned from his experience writing online, emphasizing that expertise exists on a spectrum and that sharing knowledge can be beneficial even for those who don't consider themselves ex…
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Eugene Yan shares insights on writing about machine learning and data science
Eugene Yan, an applied scientist at Amazon, is recognized for his ability to explain complex machine learning and data science concepts through his personal blog. Initially starting his website for personal development,…
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Eugene Yan explains how to bootstrap labels for search relevance
Eugene Yan's blog post addresses a reader's question about bootstrapping labels for semantic search systems without relying on expensive human annotators. Yan suggests starting with traditional lexical search methods li…
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Eugene Yan shares system design talks for RecSys and Search
Eugene Yan recently presented on system design for recommendation systems and search at two separate meetups: the MLOps Community and SF Big Analytics. The talks, which occurred in September 2021 and July 2021 respectiv…
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Data scientists can influence without authority using data and Socratic questioning
Eugene Yan's article offers strategies for data scientists to influence decisions without formal authority, emphasizing the use of data and the Socratic method. He suggests leveraging quantitative and qualitative data t…
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Eugene Yan explores lexical, graph, and embedding methods for search query matching
Eugene Yan's article explores three primary methods for matching search queries to documents: lexical, graph, and embedding-based approaches. Lexical methods involve direct query string manipulation like normalization, …
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Author shares strategies for overcoming imposter syndrome despite achievements
Susan Shu shares her personal experiences with imposter syndrome, a feeling of inadequacy despite achievements. She recounts struggling with self-doubt during her master's program at the University of Toronto and after …
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Eugene Yan shares strategies for managing chronic imposter syndrome
Eugene Yan discusses the persistent feeling of imposter syndrome, even among highly accomplished individuals. He shares personal anecdotes from his career, highlighting how a lack of traditional credentials led to feeli…
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Planning Your Career: Values and Superpowers
Eugene Yan's article discusses how understanding personal values and identifying "superpowers" can guide career development. He emphasizes the importance of self-reflection to discern what truly matters, distinguishing …
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Eugene Yan shares life lessons learned from machine learning on TalkPython podcast
Eugene Yan shared life lessons derived from machine learning concepts on the Talk Python to Me podcast. He drew parallels between ML principles and personal development, such as using data cleaning as a metaphor for ass…
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Eugene Yan shares seven habits, including reading, that shaped his decade
Eugene Yan's recent blog post outlines seven habits that have significantly influenced his past decade, emphasizing the power of consistent practice. He draws an analogy between books and pre-trained machine learning mo…
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Eugene Yan details how to write design docs for ML systems
Eugene Yan's article outlines a structured approach to creating design documents for machine learning systems, emphasizing their role in clarifying thought and facilitating feedback. The author suggests a 'Why, What, Ho…
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Eugene Yan shares framework for writing effective data science design documents
Eugene Yan's article outlines a framework for effective technical writing, particularly for data science and machine learning projects. He emphasizes the importance of detailed documentation, drawing parallels to Amazon…
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Feature Stores: A Hierarchy of Needs
Eugene Yan's article explores the concept of feature stores in machine learning, drawing an analogy to Maslow's Hierarchy of Needs. The author posits that managing features is a significant bottleneck in deploying ML mo…