The AI in fail (sponsored by Teradata)

Description

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.