AWS ML Heroes in 15: Responsible ML on Amazon SageMaker- AWS Machine Learning in 15

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Published Feb 20, 2025

Learn from AWS ML Hero Francesco Pochetti on his latest project how he trained, optimized and deployed a privacy-first, Responsible ML computer vision segmentation model on Amazon SageMaker with NVIDIA TensorRT and NVIDIA Triton.

Learning Objectives:
* Objective 1: How to conceive of and develop a Responsible ML POC project.
* Objective 2: How to train an image segmentation model (UNET) using IceVision and a sample of face synthetic dataset.
* Objective 3: How to deploy a TorchScript model to an Amazon SageMaker real-time endpoint, deploy a TensorRT model to SageMaker on top of NVIDIA’s Triton inference server and compare performance between the two methods.

***To learn more about the services featured in this talk, please visit: https://aws.amazon.com/machine-learning/responsible-machine-learning/
To download the slides visit: https://pages.awscloud.com/rs/112-TZM-766/images/2023_0201-SN-MCL_Slide-Deck.pdf

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