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: Hanlin Fang is an accomplished and dedicated product leader. She combined her unique experience of over 15 years in engineering and product management experience with emerging technology companies. Currently Hanlin is the head of machine learning product management organization at Workday, a SaaS provider for human capital and financials management solutions. Hanlin is responsible for leading machine learning product strategy and driving product execution across Workday’s product portfolio with customer-centric designs.
Hanlin has been always passionate about how technology innovation enables business disruptions and industry revolutions. Her career has spanned from Internet infrastructure, SaaS applications, IoT analytics platform, to machine learning across different companies.