Serverless Machine Learning With TensorFlow


This talk will show how to use TensorFlow with serverless platforms to bring the benefits of serverless (elastic scalability, low pricing and no charge for idle) to real-time machine learning in the cloud. Attendees will learn why serverless platforms are great for machine learning in the cloud, understand the different approaches for deploying pre-trained TensorFlow models in serverless runtimes and how to architect scalable serverless functions for performance when using TensorFlow. Attendees do not need any prior experience with machine learning or serverless cloud platforms.

Language: English

Level: Intermediate

James Thomas

Developer Advocate - IBM

James Thomas is a Developer Advocate for IBM’s Cloud division. James is a huge fan of all thing serverless! He spends his time speaking at conferences, blogging or writing open-source code to share the awesomeness of serverless cloud platforms. James is a contributor to Apache OpenWhisk, the open-source serverless platform. He wrote (and maintains) the official JavaScript client library. He also created the OpenWhisk provider plugin for The Serverless Framework.

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