eBook | Building Effective Machine Learning Teams

Machine learning (ML) and AI, by their nature, are iterative and complex. This complexity isn’t limited to the challenges inherent in data science. It also extends to the processes, teamwork, and communication required for machine learning and AI projects to be successful.

This eBook explains the three critical traits of successful ML teams, and the risks of missing any of these items:

Visibility - ability to view, access, and react to machine learning processes and deliverables
Reproducibility - one of the cornerstones of science: the ability to take an experiment, repeat the steps, and reach the same conclusion
Collaboration - communications and teamwork across multiple, often disjointed units

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“Zappos utilizes AI to improve our products, customer satisfaction and internal business processes. Using Comet and other resources we built a ML model that reduced the likelihood of order returns due to sizing issues by about 10%, which resulted in lower cost of returns. Comet has aided our success with ML and serves to further ML development within Zappos."

-- Kyle Anderson, Director of Software Engineering at Zappos

Building Effective Machine Learning Teams

Building Effective Machine Learning Teams

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