Abstract: An overview of Arize AI’s ML Observability platform, which helps ML teams automatically surface issues, understand and resolve why they occurred, and improve model performance continuously. Gabriel Barcelos, Senior Software Engineer at Arize AI, will demonstrate how to gain a centralized view of all your models in production and automatically monitor key performance attributes, data quality, and drift. Gabriel will leverage the Arize performance dashboard to take a step deeper into root cause analysis, and highlight feature drift and prediction drift impact. Utilize the Arize platform to surface issues easily, troubleshoot the root cause, and quickly resolve and improve your models as they go from research to production.
Bio: Gabe Barcelos is a founding engineer at Arize AI, a machine learning observability company, specializing in ML frameworks and data systems. Prior to Arize, he led foundational data pipeline initiatives and an industry-recognized customer service team at Adobe, TubeMogul, and Saildrone. From autonomous research drones to digital advertising bidding and analytics systems, Gabe strives to infuse a data-driven focus and customer-centric mindset into programs he oversees. In his free time, you’ll find Gabe cooking, skiing, or exploring new trails with his wife and dog (who’s a very good girl). He holds a bachelor's degree in chemical engineering from UC Berkeley.
