Sep 23–26, 2019
Shelbee Eigenbrode

Shelbee Eigenbrode
Solutions Architct, Amazon Web Services

Shelbee Eigenbrode is a solutions architect at Amazon Web Services (AWS). Her current areas of depth include DevOps combined with machine learning and artificial intelligence. She’s been in technology for 22 years, spanning multiple roles and technologies. Previously, she spent 20+ years at IBM. She’s a published author, blogger, and vlogger evangelizing DevOps practices with a passion for driving rapid innovation and optimization at scale. In 2016, she won the DevOps dozen blog of the year demonstrating what DevOps is not. With over 26 patents granted across various technology domains, her passion for continuous innovation combined with a love of all things data recently turned her focus to data science. Combining her backgrounds in data, DevOps, and machine learning, her passion is helping customers embrace data science and ensure all data models have a path to use. She also aims to put ML in the hands of developers and customers who are not classically trained data scientists.

Sessions

5:25pm6:05pm Wednesday, September 25, 2019
Location: 3B - Expo Hall
Sireesha Muppala (Amazon Web Services), Shelbee Eigenbrode (Amazon Web Services), Emily Webber (Amazon Web Services)
Mansplaining. Know it? Hate it? Want to make it go away? Sireesha Muppala, Shelbee Eigenbrode, and Emily Webber tackle the problem of men talking over or down to women and its impact on career progression for women. They also demonstrate an Alexa skill that uses deep learning techniques on incoming audio feeds, examine ownership of the problem for women and men, and suggest helpful strategies. Read more.
3:45pm4:25pm Thursday, September 26, 2019
Location: 1A 21/22
Sireesha Muppala (Amazon Web Services), Shelbee Eigenbrode (Amazon Web Services), Randall DeFauw (Amazon Web Services)
As an increasing level of automation becomes available to data science, the balance between automation and quality needs to be maintained. Applying DevOps practices to machine learning workloads brings models to the market faster and maintains the quality and integrity of those models. Sireesha Muppala, Shelbee Eigenbrode, and Randall DeFauw explore applying DevOps practices to ML workloads. Read more.

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