"Most ML projects fail to reach production. Five recurring pitfalls drive failures in ML projects: choosing the wrong problem, data quality/labeling issues, the model-to-product gap, offline-online mismatch, and non-technical blockers. Define a clear business goal before starting, and validate that it truly needs ML. Translating business goals into ML requires heavy data engineering, objective-function design, and sometimes expensive infrastructure, making late pivots costly."
"Treat data as a product: prevent leakage and bias, invest in labeling and golden sets, and build evaluation pipelines early to avoid brittle releases. Manage uncertainty with a balanced portfolio: ship low-risk/high-impact wins to justify investment, while incubating riskier bets that can be game-changing. Encourage early collaboration and active engagement of cross-functional teams. Successful ML teams align stakeholders, scope an MVP, build end-to-end early for A/B testing, and iterate based on monitoring."
Most ML projects fail to reach production due to recurring pitfalls including wrong problem selection, poor data quality and labeling, and gaps between models and products. Effective ML requires a clear business goal and validation that ML is necessary, since translating goals into ML demands heavy data engineering, objective-function design, and sometimes costly infrastructure. Treat data as a product by preventing leakage and bias, investing in labeling and golden sets, and building evaluation pipelines early. Manage uncertainty with a balanced portfolio of low-risk wins and high-risk incubations. Encourage cross-functional collaboration, scope an MVP, enable end-to-end testing, and iterate using monitoring.
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