This talk will show how to use TensorFlow with serverless platforms to bring the benefits of serverless runtimes (elastic scalability, low pricing and no charge for idle) to the task of 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 models and learn how to architect scalable serverless functions when using TensorFlow.
Key issues covered will include loading TensorFlow libraries in serverless runtimes, tips on improving performance on cold and warm starts and how model scoring without a GPU affects throughput. Different methods for running TF models with serverless runtimes, including TensorFlow JS, will be compared and contrasted.
Developers do not need any prior experience with machine learning or serverless cloud platforms. This talk is applicable for all serverless developers interested in machine learning, rather than being restricted to a single platform or vendor.