The Metagame of Applying Machine Learning
Eugene Yan
An applied scientist discusses the metagame of deploying machine learning in industry, using the analogy of exploiting lottery rules to illustrate how understanding systems beyond core techniques drives results. Key practices include starting from business problems rather than technology, prioritizing system and training data design over model architecture, establishing clear objectives and measurements through A/B testing, and building stakeholder trust through communication. Success in applied ML requires respecting existing systems, continuous learning, and treating failures as learning opportunities.
Why it matters
How to go from knowing machine learning to applying it at work to drive impact.
