Stephanie Kirmer

Stephanie Kirmer

Senior Machine Learning Engineer at DataGrail

    Stephanie Kirmer is a columnist at Towards Data Science and a senior machine learning engineer at DataGrail, a company committed to helping businesses protect customer data and minimize risk. She has almost a decade of experience building machine learning solutions in industry, and before going into data science she was an adjunct professor of sociology and higher education administrator at DePaul University. She brings a unique mix of social science perspective and deep technical and business experience to writing and speaking accessibly about today's challenges around AI and machine learning. Learn more at or

    All Sessions by Stephanie Kirmer

    West Talks 07/23/2024

    “Just Do Something with AI”: Bridging the Business Communication Gap for ML Practitioners

    <span class="etn-schedule-location"> <span class="firstfocus">Machine Learning</span> <span class="secfocus">All Levels</span> </span>

    Bringing machine learning functions within a business into alignment with the rest of the organization can be a struggle, especially now when AI has advanced so much technologically, and become such an important part of business innovation and success. In this talk, I'll discuss how ML teams and other business areas fail to communicate effectively, why AI is so misunderstood by laypeople, and why this can lead to the failure of business critical AI initiatives. To solve these problems, ML/AI professionals need to take the initiative to communicate about what they do and what value they can contribute. Educated, smart laypeople are frankly confused and misinformed about what AI is, which leads to unachievable expectations and eventual disappointment, even when machine learning is successfully implemented. At the same time, ML/AI professionals need to deeply understand the goals of the business in order to build AI solutions that will meet those goals. AI represents a significant change to business technology, and massive potential for productivity and problem solving, but as practitioners know only too well, it’s not magic. Attendees to this talk will learn how to bridge the gap between business and AI, and will learn key lessons about how to design and deploy AI initiatives to achieve business success.

    Open Data Science




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