Last week, we caught up with Michael Tambe, Head of Data Science of Amazon Advertising Field Sales, to discuss his views on some of the challenges and opportunities that data scientists face working with real world AI / ML solutions. Michael has been a data science leader for over 8 years, focused on building data science teams in go-to-market areas within sales, marketing, and pricing. Here’s the advice that Michael has for other business leaders looking to deploy AI solutions and the best practices on optimizing ML model performance.
Deploying AI instantly brought value and growth to many businesses. However, it is well established that sustaining the value over time, not to mention maximizing it, could be quite challenging. Continuous optimization is the key to successful AI deployments. Beginning with a product that’s good enough, learning from how it performs in the real world, especially as the world (read: the data environment) changes, and then improving; then learning and improving again and so on. It’s a bit of an obvious insight but it is rare for AI-driven products to be perfect from day one.
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At Mona, we strive to enable better visibility into AI systems in order to reduce the associated risk with production AI, optimize the operational processes around versions and releases, and plan better AI roadmaps using feedback based on production data.