A Tale of 3 Deep Learning Frameworks: TensorFlow, Keras, & PyTorch with Jules Damji & Brooke Wenig

The explosion of data volume in the years to come challenge the idea of a centralized cloud infrastructure which handles all business needs. Edge computing comes to rescue by pushing the needs of computation and data analysis at the edge of the network, thus avoiding data exchange when makes sense. One of the areas where data exchange could impose a big overhead is scoring ML models especially where data to score are files like images eg. in a computer vision application. Another concern in some applications, is that of keeping data as private as possible and this is where keeping things local makes sense. In this talk we will discuss current needs and recent advances in model serving, like newly introduced formats for pushing models at the edge nodes eg. mobile phones and how a unified model serving architecture could cover current and future needs for both data scientists and data engineers. This architecture is based among others, on training models in a distributed fashion with TensorFlow and leveraging Spark for cleaning data before training (eg. using TensorFlow connector). Finally we will describe a microservice based approach for scoring models back at the cloud infrastructure side (where bandwidth can be high) eg. using TensorFlow serving and updating models remotely with a pull model approach for edge devices. We will talk also about implementing the proposed architecture and how that might look on a modern deployment environment eg. Kubernetes.

About: Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business.

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[markrex]

Thanks a lot .. this was quite useful.