Join us for an evening of Bay Area Apache Spark Meetup at the Spark + AI Summit (https://databricks.com/sparkaisummit/north-america) featuring tech-talks from Google Inc and Workday Inc on Machine Learning.
Thanks to #WomenInUnifiedAnalytics & Diversity Team at Databricks for sponsoring this meetup.
(Note: This meetup is open to everyone. You don’t have to be registered for Spark + AI Summit.)
6:00 - 6:30 pm Happy Hour: Mingling & Refreshments
6:30 - 6:40 pm Opening Remarks (Jules Damji, Databricks)
6:40 - 7:25 pm Talk-1: Paige Bailey, Google Inc
7:25 - 8:05 pm Talk-2: Madhura Dudhgaonkar, Workday, Inc
8:05 - 9:00 pm More Mingling & Networking
Talk 1: Details coming soon...
Paige Bailey (https://www.linkedin.com/in/dynamicwebpaige) is a TensorFlow Developer Advocate at Google, based in Mountain View, CA. Prior to joining Google, Paige worked as a senior software engineer in the office of the Azure CTO; as a Cloud Developer Advocate for machine learning at Microsoft; and as a data scientist for Chevron in Houston, TX.
Paige has over a decade of experience using Python for data analysis, five years of experience doing machine learning - and can't wait to show you about the new capabilities in TensorFlow 2.0.
Talk 2: Machine Learning Products - How do you begin and when do you scale?
Abstract: So you have heard all the hype around how Machine Learning is going to change the world. But within your business context, where do you start? How do you choose the right use cases to begin with? How do you get leadership buy-in for more investment? And when do you start thinking about scaling your ML Services?
In this session, you will walk away with an actionable framework to bootstrap and scale an applied machine learning services function. You will see the framework in action through an actual 0 to 1 product journey involving deep learning where we delivered value in record speed in spite of not having a dataset when we started. You will get practical tips on how to make decisions about when and how to scale your capability to scale ML Services and platform.
Furthermore, how to get leadership buy-in for more investment? You will go back with some counter-intuitive tips that we discovered as a result of productizing ML services over the last 5+ years using a diverse range of technologies: Vision, Language, Graph, Anomaly Detection, Search Relevance, Personalization.
Madhura Dudhgaonkar (https://www.linkedin.com/in/madhurad) is a Machine Learning leader at Workday passionate about modernizing the future of work. Her team, a pioneer in the Enterprise Machine Learning space, has spent 6+ years building ML products leveraging Vision, Natural Language Processing, Recommendations, Anomaly Detection and more.
Madhura’s career journey goes from being a hands-on engineer to leading large organizations across SUN Microsystems, Adobe and now Workday. Her background covers building both consumer and enterprise products - the latest of them involving multiple 0 to 1 product journeys leveraging Machine Learning.
She is 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. She also is passionate about building diverse teams and leads diversity and inclusion work via leading a Women at Workday chapter.