https://www.infoq.com InfoQ QCon San Francisco 2024 feed https://www.infoq.com/articles/why-ml-projects-fail-production/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=QCon+San+Francisco+2024 <img src="https://res.infoq.com/articles/why-ml-projects-fail-production/en/headerimage/why-ml-projects-fail-production-header-1769072877092.jpg"/><p>In this article, the author diagnoses common failures in ML initiatives, including weak problem framing and the persistent prototype-to-production gap. The piece provides practical, experience-based guidance on setting clear business goals, treating data as a product, and aligning cross-functional teams for reliable, production-ready ML delivery.</p> <i>By Wenjie Zi</i> QCon Software Development Conference Machine Learning Artificial Intelligence AI Development QCon San Francisco 2024 AI, ML & Data Engineering Culture & Methods Development article Mon, 02 Feb 2026 11:00:00 GMT https://www.infoq.com/articles/why-ml-projects-fail-production/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=QCon+San+Francisco+2024 Wenjie Zi 2026-02-02T11:00:00Z /articles/why-ml-projects-fail-production/en