30 min
Episode 046 - Computer Vision on AWS with Francesco Pochetti – Part 2 AWS Developers Podcast
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- Technology
In part two, Dave chats again with Francesco Pochetti, Senior Machine Language Engineer at Bolt, and an AWS Machine Learning Hero. In this episode, Francesco dives deep in the ML tools on AWS, starting with the tools such as NVIDIA Triton and TensorRT, and how to improve processing time for Computer Vision. He also covers Amazon SageMaker, and many other AWS ML services as well as deploying ML models using Docker in the best way possible. If you missed it, you could listen to part one of this conversation in Episode 045.
Francesco on Twitter: https://twitter.com/Fra_Pochetti
Dave on Twitter: https://twitter.com/thedavedev
Francesco’s Website: https://francescopochetti.com/
Francesco’s LinkedIn: https://www.linkedin.com/in/francescopochetti/
Francesco’s GitHub: https://github.com/FraPochetti
[BLOG] Blurry faces: Training, Optimizing and Deploying a segmentation model on Amazon SageMaker with NVIDIA TensorRT and NVIDIA Triton - https://francescopochetti.com/blurry-faces-a-journey-from-training-a-segmentation-model-to-deploying-tensorrt-to-nvidia-triton-on-amazon-sagemaker/
[BLOG] Machine Learning and Developing inside a Docker Container in Visual Studio Code https://francescopochetti.com/developing-inside-a-docker-container-in-visual-studio-code/
[BLOG] Deploying a Fashion-MNIST web app with Flask and Docker: https://francescopochetti.com/deploying-a-fashion-mnist-web-app-with-flask-and-docker/
[BLOG] IceVision meets AWS: detect LaTeX symbols in handwritten math and deploy with Docker on Lambda: https://francescopochetti.com/icevision-meets-aws-detect-latex-symbols-in-handwritten-math-and-deploy-with-docker-on-lambda/
[DOCS] Amazon Rekognition - https://aws.amazon.com/rekognition/
[DOCS] Amazon SageMaker - https://aws.amazon.com/sagemaker/
[DOCS] Amazon Textract - https://aws.amazon.com/textract/
[DOCS] Deploy fast and scalable AI with NVIDIA Triton Inference Server in Amazon SageMaker https://aws.amazon.com/blogs/machine-learning/deploy-fast-and-scalable-ai-with-nvidia-triton-inference-server-in-amazon-sagemaker/
[GIT] Nvidia Triton Inference Server:
https://github.com/triton-inference-server/server/
[GIT] Blurry faces: Training, Optimizing and Deploying a segmentation model on Amazon SageMaker with NVIDIA TensorRT and NVIDIA Triton - https://github.com/FraPochetti/KagglePlaygrounds/tree/master/triton_nvidia_blurry_faces
Subscribe:
Amazon Music: https://music.amazon.com/podcasts/f8bf7630-2521-4b40-be90-c46a9222c159/aws-developers-podcast
Apple Podcasts: https://podcasts.apple.com/us/podcast/aws-developers-podcast/id1574162669
Google Podcasts:
https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5zb3VuZGNsb3VkLmNvbS91c2Vycy9zb3VuZGNsb3VkOnVzZXJzOjk5NDM2MzU0OS9zb3VuZHMucnNz
Spotify:
https://open.spotify.com/show/7rQjgnBvuyr18K03tnEHBI
TuneIn:
https://tunein.com/podcasts/Technology-Podcasts/AWS-Developers-Podcast-p1461814/
RSS Feed:
https://feeds.soundcloud
In part two, Dave chats again with Francesco Pochetti, Senior Machine Language Engineer at Bolt, and an AWS Machine Learning Hero. In this episode, Francesco dives deep in the ML tools on AWS, starting with the tools such as NVIDIA Triton and TensorRT, and how to improve processing time for Computer Vision. He also covers Amazon SageMaker, and many other AWS ML services as well as deploying ML models using Docker in the best way possible. If you missed it, you could listen to part one of this conversation in Episode 045.
Francesco on Twitter: https://twitter.com/Fra_Pochetti
Dave on Twitter: https://twitter.com/thedavedev
Francesco’s Website: https://francescopochetti.com/
Francesco’s LinkedIn: https://www.linkedin.com/in/francescopochetti/
Francesco’s GitHub: https://github.com/FraPochetti
[BLOG] Blurry faces: Training, Optimizing and Deploying a segmentation model on Amazon SageMaker with NVIDIA TensorRT and NVIDIA Triton - https://francescopochetti.com/blurry-faces-a-journey-from-training-a-segmentation-model-to-deploying-tensorrt-to-nvidia-triton-on-amazon-sagemaker/
[BLOG] Machine Learning and Developing inside a Docker Container in Visual Studio Code https://francescopochetti.com/developing-inside-a-docker-container-in-visual-studio-code/
[BLOG] Deploying a Fashion-MNIST web app with Flask and Docker: https://francescopochetti.com/deploying-a-fashion-mnist-web-app-with-flask-and-docker/
[BLOG] IceVision meets AWS: detect LaTeX symbols in handwritten math and deploy with Docker on Lambda: https://francescopochetti.com/icevision-meets-aws-detect-latex-symbols-in-handwritten-math-and-deploy-with-docker-on-lambda/
[DOCS] Amazon Rekognition - https://aws.amazon.com/rekognition/
[DOCS] Amazon SageMaker - https://aws.amazon.com/sagemaker/
[DOCS] Amazon Textract - https://aws.amazon.com/textract/
[DOCS] Deploy fast and scalable AI with NVIDIA Triton Inference Server in Amazon SageMaker https://aws.amazon.com/blogs/machine-learning/deploy-fast-and-scalable-ai-with-nvidia-triton-inference-server-in-amazon-sagemaker/
[GIT] Nvidia Triton Inference Server:
https://github.com/triton-inference-server/server/
[GIT] Blurry faces: Training, Optimizing and Deploying a segmentation model on Amazon SageMaker with NVIDIA TensorRT and NVIDIA Triton - https://github.com/FraPochetti/KagglePlaygrounds/tree/master/triton_nvidia_blurry_faces
Subscribe:
Amazon Music: https://music.amazon.com/podcasts/f8bf7630-2521-4b40-be90-c46a9222c159/aws-developers-podcast
Apple Podcasts: https://podcasts.apple.com/us/podcast/aws-developers-podcast/id1574162669
Google Podcasts:
https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5zb3VuZGNsb3VkLmNvbS91c2Vycy9zb3VuZGNsb3VkOnVzZXJzOjk5NDM2MzU0OS9zb3VuZHMucnNz
Spotify:
https://open.spotify.com/show/7rQjgnBvuyr18K03tnEHBI
TuneIn:
https://tunein.com/podcasts/Technology-Podcasts/AWS-Developers-Podcast-p1461814/
RSS Feed:
https://feeds.soundcloud
30 min