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DataFramed

#179 Why ML Projects Fail, and How to Ensure Success with Eric Siegel, Founder of Machine Learning Week, Former Columbia Professor, and Bestselling Author

Feb 5, 2024
47:23
Snipd AI
Eric Siegel, founder of Machine Learning Week and best selling author discusses why ML projects fail and how to ensure success, the challenges in machine learning project deployment, collaboration challenges between data and business teams, leveraging machine learning for route optimization with UPS, the importance of change management and skills transformation, and the hype and potential dangers of generative AI.
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Quick takeaways

  • Collaboration between data teams and business stakeholders is crucial for successful machine learning deployment and requires a standardized practice known as BizML.
  • Business stakeholders should focus on quantifying the impact of incremental improvements in machine learning performance using business metrics, bridging the gap between technical and business perspectives.

Deep dives

Importance of Collaboration and Scoping Machine Learning Use Cases with Business Stakeholders

Machine learning use cases need to be scoped in collaboration with business stakeholders. This collaboration, also known as BizML, is an organizational issue that requires a standardized practice. The need for a standard business paradigm or playbook is crucial to ensure successful deployment of machine learning projects. It is important for business stakeholders to have a semi-technical knowledge, including understanding what is predicted, how well it is predicted, and what is done about it. By involving business stakeholders from the beginning of the project and speaking the same language, organizations can increase the chances of successful machine learning deployment and value-driven projects.

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