A framework to bootstrap and scale a machine learning function
Who is this presentation for?
- Data scientists, machine learning engineers, data science managers, and machine learning product managers
With the hype around artificial intelligence and machine learning sky high and the perceived talent shortage, it’s still extremely hard for organizations to build their machine learning efforts.
Madhura Dudhgaonkar breaks down the lessons learned into an an actionable framework to bootstrap and scale a machine learning product function. You’ll see the framework in action through an actual 0 to 1 product journey, involving deep learning where value was delivered in record speed in spite of not having a dataset. You’ll hear counterintuitive tips that Madhura has been using to productize ML services over the last 5+ years (using a diverse range of technologies—vision, language, graph, anomaly detection, search relevance, and personalization).
- A basic understanding of the machine learning discipline
What you'll learn
- See an actionable framework to begin and scale your ML product journey
- Discover tips on all sides of the equation for bootstrapping and scaling ML functions
Madhura Dudhgaonkar (Hiring)
Madhura Dudhgaonkar is a machine learning leader at Workday, where she’s passionate about modernizing the future of work. She’s part of the Workday ML organization, a pioneer in the enterprise machine learning space, and has spent 5+ years building ML products leveraging vision, natural language processing, recommendations, anomaly detection, and more. Previously, Madhura’s work ranged from a hands-on engineer to leading large organizations across Sun Microsystems, Adobe, and Workday. Her background covers building consumer and enterprise products—the latest involving multiple 0 to 1 product journeys leveraging machine learning. She’s considered a thought leader in building ML products and is frequently invited to speak at AI conferences. Madhura holds a master’s degree in math and computer science. When not obsessing over technology, she can be found outdoors, running, hiking, or snowboarding.
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