26 min
Episode 045 - Computer Vision on AWS with Francesco Pochetti AWS Developers Podcast
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- Technology
In this episode, Dave chats with Francesco Pochetti, Senior Machine Language Engineer at Bolt, and an AWS Machine Learning Hero. Francesco covers his career start as a chemist, his journey into a career of Data Science, and how Computer Vision technology is handling some of the most difficult Machine Learning problems today.
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 this episode, Dave chats with Francesco Pochetti, Senior Machine Language Engineer at Bolt, and an AWS Machine Learning Hero. Francesco covers his career start as a chemist, his journey into a career of Data Science, and how Computer Vision technology is handling some of the most difficult Machine Learning problems today.
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
26 min