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G

Speaker

Greg Michaelson, PhD

2

talks

Cofounder, Zerve.ai

Greg Michaelson is Cofounder and Chief Product Officer at Zerve, a young, stealthy startup that’s rethinking the data science development experience. Previously, Greg was an early joiner at DataRobot where he played many roles, including Chief Customer Officer. Prior to that, he worked as a data scientist in the financial sector after earning a PhD in applied statistics from the University of Alabama. In his spare time, Greg manufactures a line of flavored breakfast cereal toppings called Cerup. He lives in Spring Creek, Nevada with his wife, four children, and two Clumber Spaniels.

Bio from: Data Universe 2024

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Technical Debt is entropy applied to technology. Systems age, standards change, mistakes occur, and updates happen. The moment you buy any technology, you have to maintain it—but since technical failure is often hard to detect until something catastrophic occurs, systems begin to drift away from their initial operating state.

The faster an industry changes, the more quickly technical debt accrues. A farm tractor in the 1960s might still be in service today, because relatively little variance has happened; but a modern, GPS-guided piece of farm equipment needs constant software updates. And IT is changing much more quickly than tractors.

But constant support means rising costs in an organization asked to do more with less. Invest in maintenance and you'll be reliant on outdated systems amidst spiraling expenses; neglect them, and you'll constantly be chasing the new thing while scrambling to repair outages on legacy systems.

What's a savvy CIO to do? In this panel discussion, DM Radion's Eric Kavanagh hathers a panel of experts on digital transformation, AI-infused metamorphosis, and the role emerging technology plays in clearing the clutter of outdated IT stacks.

Discover the hidden power of feature engineering in revolutionizing machine learning performance. This talk explores how crafting informative features transforms model outcomes, offering practical techniques and real-world examples. From understanding data intricacies to optimizing model efficacy, learn why feature engineering is the ultimate key to enhancing machine learning success.