Operationalizing Machine Learning at Scale at Starbucks - Databricks

Operationalizing Machine Learning at Scale at Starbucks

As ML-driven innovations are propelled by the Self-Service capabilities in the Enterprise Data and Analytics Platform, teams face a significant entry barrier and productivity issues in moving from POCs to Operating ML-powered apps at scale in production. This talk is the journey of a team in using the Starbucks AI foundational capabilities in EDAP to deploy, manage and operate ML models as secure and scalable cognitive services that have the potential of powering internet-scale inferences for use cases and applications.



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About Balaji Venkataraman

Starbucks

Balaji R Venkataraman is an Engineering Manager with the Enterprise Data And Analytic Platform team at Starbucks. His team ships and operates on-demand platforms on the Azure cloud that powers petabyte scale Data Engineering, at scale ML/AI development and Operationalization across Starbucks. These offerings shape multiple Next generation personalization and retail optimization initiatives.

About Denny Lee

Databricks

Denny Lee is a Developer Advocate at Databricks. He is a hands-on distributed systems and data sciences engineer with extensive experience developing internet-scale infrastructure, data platforms, and predictive analytics systems for both on-premise and cloud environments. He also has a Masters of Biomedical Informatics from Oregon Health and Sciences University and has architected and implemented powerful data solutions for enterprise Healthcare customers. His current technical focuses include Distributed Systems, Apache Spark, Deep Learning, Machine Learning, and Genomics.