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/Artificial Intelligence. She has been in technology for 22 years spanning multiple roles and technologies. She spent 20+ years at IBM and joined AWS in November of 2018 She is a published author, blogger/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 has recently turned her focus to the field of Data Science. Combining her backgrounds in Data, DevOps and Machine Learning, her current passion is to help customers not only embrace data science but also to ensure all data models have a path to being utilized. She also aims to put ML is the hands of developers and customers that are not classically trained data scientists. Over her career, she has held senior leadership positions and has a passion for mentorship.


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
Sireesha Muppala (Amazon Web Services), Shelbee Eigenbrode (Amazon Web Services), Randall DeFauw (Amazon Web Services)
As an increasing level of automation is becoming available to data science, there is a balance between automation and quality that needs to be maintained. Applying DevOps practices to machine learning workloads not only brings models to the market faster but also maintains the quality and integrity of those models. This presentation will focus on applying DevOps practices to ML workloads. Read more.

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