Top challenges and considerations for adopting Machine Learning at Scale

Description
Speaker
In this session, we will talk about the top challenges organizations run into when adopting machine learning at scale.
As organizations complete their first few successful prototypes and begin to realize the benefits of using machine learning to drive business outcomes, the next question is usually “How can we adopt and scale our machine learning strategy to drive more business outcomes?”.
To address this question, we’ll address the top challenges and considerations when approaching these challenges. We’ll discuss choosing the right projects and MLOps among other topics in this session.
Shelbee Eigenbrode (AWS)

Principal AI and Machine Learning Specialist Solutions Architect at Amazon Web Services (AWS). She holds 6 AWS certifications and has been in technology for 23 years spanning multiple industries, technologies, and roles. She is currently focusing on combining her DevOps and ML background to deliver and manage ML workloads at scale. With over 35 patents granted across various technology domains, she has a passion for continuous innovation and using data to drive business outcomes..
  • Date: Sep 09, 10:00 (US Pacific Time)
  • Fee: Free
  • Available Seats: 334 (max 500)
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