Episode 045 - Computer Vision on AWS with Francesco Pochetti

Jul 15, 2022

Francesco Pochetti, Senior Machine Language Engineer at Bolt and an AWS Machine Learning Hero, discusses his transition from chemist to computer vision engineer, the value of diverse learning in machine learning, and challenges of computer vision on AWS such as face matching and OCR. They also cover topics like network bandwidth challenges, deploying models on SageMaker, optimizing neural networks, and face blurring for privacy.

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Introduction

00:00 • 2min

Transitioning from Chemistry to Data Science and Machine Learning

01:37 • 3min

The Value of Diverse Learning and Understanding Business Problems in Machine Learning

04:34 • 2min

Computer Vision on AWS: Face Matching, OCR, and Challenges

07:03 • 14min

Challenges of Network Bandwidth, Deploying Models on SageMaker, and Face Blurring for Privacy

20:57 • 5min

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