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Speaker "Chris Fregly" Details Back

 

Topic

Using AWS SageMaker, Kubernetes, and PipelineAI for High Performance, Hybrid-Cloud Distributed TensorFlow Model Training and Serving with GPUs.

Abstract

In this talk, I will demonstrate how to train, optimize, and serve distributed machine learning models across various environments including the following:

1) Local Laptop

2) Kubernetes Cluster (Running Anywhere)

3) AWS's New SageMaker Service

I'll also present some post-training model-optimization techniques to improve model serving performance for TensorFlow running on GPUs. These techniques include 16-bit model training, neural network layer fusing, and 8-bit weight quantization.

Lastly, I'll discuss alternate runtimes for TensorFlow on GPUs including and TensorFlow Lite and Nvidia's TensorRT.

Profile

Chris Fregly is a Developer Advocate for AI and Machine Learning at Amazon Web Services (AWS) based in San Francisco, California. He is co-author of the O'Reilly Book, "Data Science on AWS."
 
Chris is also the Founder of many global meetups focused on Apache Spark, TensorFlow, and KubeFlow. He regularly speaks at AI and Machine Learning conferences across the world including O’Reilly AI & Strata, Open Data Science Conference (ODSC), and GPU Technology Conference (GTC).
 
Previously, Chris was Founder at PipelineAI where he worked with many AI-first startups and enterprises to continuously deploy ML/AI Pipelines using Apache Spark ML, Kubernetes, TensorFlow, Kubeflow, Amazon EKS, and Amazon SageMaker.