Simplify Distributed TensorFlow Training for Fast Image Categorization at Starbucks - Databricks

Simplify Distributed TensorFlow Training for Fast Image Categorization at Starbucks

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In addition to the many data engineering initiatives at Starbucks, we are also working on many interesting data science initatives. The business scenarios involved in our deep learning initatives include (but are not limited to) planogram analysis (layout of our stores for efficient partner and customer flow) to predicting product pairings (e.g. purchase a caramel machiato and perhaps you would like caramel brownie) via the product components using graph convolutional networks.

For this session, we will be focusing on how we can run distributed Keras (TensorFlow backend) training to perform image analytics. This will be combined with MLflow to showcase the data science lifecycle and how Databricks + MLflow simplifies it.


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About Vishwanath Subramanian

Vishwanath Subramanian is an Director of Data and Analytics Engineering at Starbucks. Vishwanath has over 15 years of  experience with a background in distributed systems, product management, software engineering and Analytics. At Starbucks, his key focus is on providing Next Generation Analytics platforms and enabling large scale data processing and machine learning to enable Business Intelligence and Data Services across Starbucks.

About Denny Lee

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.