Solution Study
Thursday, December 07
10:15 AM - 10:45 AM
Live in Dearborn, Michigan
Less Details
Today, according to McKinsey, 85% of ML models fail, leading to loss of time, revenue and business impact. Understanding common risk factors for model failure has a direct impact on improving the visibility and predictability of ML development cycles, especially for ADAS & AV applications. In this session, we will reveal the criteria executives can use to accelerate their ML models’ path to production — and keep them there. We will share best practices such as how to:
Duncan Curtis is the Chief Product Officer at Sama where he ensures the ML/AI models powering AI technology products are of the utmost quality. Previously the head of product at Zoox, VP of Product at Aptiv and Product Manager at Google, Duncan leads the teams powering ML/AI technologies for enterprises such as Walmart, Google, and NVIDIA.
The Pop in Your Job
I'm driven by the impact I get to have in my job. At Sama I get to work on three amazing areas at once: (1) Moving the AI industry forward by working with cutting-edge ML products used by billions of people every day; (2) The intersection of Human/Machine interaction — especially the work our internal ML team does to enhance what insights we can derive, how we can improve their experience and their efficiency; (3) Our social mission to eliminate poverty through meaningful work. As a Certified B Corp, we're a force for good and I love that my work changes people's lives while addressing the fact that talent is equally distributed but opportunity is not.