In this article, the author diagnoses common failures such as weak problem framing and the prototype-to-production gap, and offers practical guidance for successful ML project delivery.

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Most ML projects fail to reach production due to five recurring pitfalls: choosing the wrong problem, data quality issues, the model-to-product gap, offline-online performance mismatch, and non-technical blockers like stakeholder misalignment. Success requires defining clear business goals upfront, treating data as a product with robust evaluation pipelines, building cross-functional teams that collaborate early, and managing ML projects differently than traditional software. Portfolio balance matters: ship low-risk wins to justify investment while incubating high-risk, high-impact bets. The key is pushing to A/B testing quickly rather than over-optimizing offline metrics, and educating stakeholders about ML's inherent uncertainty and realistic costs.

Why Most Machine Learning Projects Fail to Reach Production

Failure Rates of Machine Learning Projects