Abstract: With the AI hype cycle still being pretty active, a lot of companies are still struggling with how best to bootstrap and more importantly successfully point their AI capabilities to achieve bottom line growth. A lot of executives are still struggling with - What strategic areas do I start with? What skillset should I hire? How do I choose the right leadership team to lead this function? What processes should the team focus on to get most leverage and ingrain an AI-first approach across the entire product portfolio?
Having spent the last 4+ years bootstrapping Machine Learning at Workday, we have learnt a lot about how best to take a Machine Learning function from 0 to 10. With investments in Machine Learning infrastructure and environments, building distributed Machine Learning microservices that can be used like Lego blocks and by establishing and streamlining Data Science use case pipeline processes, we have gone from being able to ship 1 product use case a year to productizing 3-5 use cases every 6 months.
In the session, we will share a deep learning use case in Workday Expense application to demonstrate how the thought process works practically and how we deliver the solution with the scale and performance to meet enterprise customer requirements.
You will walk out of this session with a practical framework that will help you take the Machine learning function from 0 to 10 in your company.
Bio: Madhura Dudhgaonkar is responsible for leading Workday’s search, data science and machine learning engineering teams. Her teams have spent 4+ years building machine learning products used by Fortune 500 companies. Her experience ranges from being a hands-on engineer to leading large engineering organizations. Madhura’s career spans across SUN Microsystems, Adobe and now Workday. During her career she has been involved with building a variety of products - from developing Java Language to building a version 1.0 consumer product to building enterprise SaaS products.
Madhura holds a master’s degree in math and computer science. She is a frequent speaker and leads diversity work via Women@Workday San Francisco Chapter. When not obsessing over technology, she can be found outdoors, running, hiking or snowboarding.
Senior Director, Data Science and Machine Learning Engineering | Workday
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