Do your AI projects fail? Are your data scientists failures? Should they be? Many teams are encouraged to document and promote successes, but how many of them accurately document the things that don’t work? With many data science teams experiencing high turnover, are your precious resources just failing in the same way as their predecessors? What about projects that have a good outcome but never make it to production—does this count as failure?
Some AI initiatives don’t work, and, with hindsight, it’s clear some never could have worked. This is the wrong kind of failure, wasting time and money. However, some AI initiatives thrive on failure. It leads to an iterative pattern of discovery that ends with fantastic results. Christopher Hillman explains why failure can be a good thing and how to encourage the right kind of failure. Chris also demonstrates how to learn from failure so that you don’t make the same mistakes over and over again.
Chris Hillman is a London-based principal data scientist on the international advanced analytics team at Teradata. Chris is involved in the presale and startup activities of analytics projects helping customers to gain value from and understand advanced analytics and machine learning. He has over 25 years’ experience working with analytics across many industries, including retail, finance, telecom, and manufacturing. He has spoken on data science and analytics at Teradata events such as Universe and PARTNERS and industry events such as the Strata, Flink Forward, and IEEE Big Data conferences. Chris holds a PhD from the University of Dundee, where he applied big data analytics to data from the Human Proteome Project.
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