SPEAKER DETAILS

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Saishruthi Swaminathan

developer.ibm.com/
Saishruthi Swaminathan is a developer advocate and data scientist in the IBM CODAIT team whose main focus is to democratize data and AI through open source technologies. She received her Bachelor degree in Electronics and Instrumentation and Masters in Electrical Engineering specializing in data science. She is also one of the active members in University AI research team. Her passion is to dive deep into the ocean of data, extract insights and use AI for social good. On a mission to spread the knowledge and experience, she acquired in her learning process through her blogs. LinkedIn : https://www.linkedin.com/in/saishruthi-swaminathan/ Medium: https://medium.com/@saishruthi.tn

Sessions

Deploy deep learning models as a web microservice in minutes

Level :
Beginner
Room :
Not Assigned
Interested : (-) - Registered : ((*))

Powering your application with deep learning is no walk in the park but is certainly attainable with some tricks and good practice. Serving a deep learning model on a production system demands the model to be stable, reproducible, capable of isolation and to behave as a stand-alone package. One possible solution to this is a containerized microservice. Ideally, serving deep learning microservices should be quick and efficient, without having to dive deep into the underlying algorithms and their implementation. Too good to be true? Not anymore! Together, we will demystify the process of developing, training, and deploying deep learning models as a web microservice using Model Asset Exchange, an open source framework developed in Python at the IBM Center for Open Source Data and AI Technologies (CODAIT). We will kick off with an overview of how deep learning models are best published as Docker Images on DockerHub and are best prepared for deployment in local or cloud environments using Kubernetes or Docker. We highlight the following benefits of such an approach: * Standardized REST API implementation and application-friendly output format (JSON) * Abstracting out the complex pre and post-processing portions of the model inputs and outputs. We will walk you through some super cool applications. All these are open source and we conclude by opening the gates for you to be a part of this amazing initiative!

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