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Sept 2023 Computer Vision Meetup (Virtual – EU and Americas)
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Zoom Link
https://voxel51.com/computer-vision-events/september-14-meetup/
From Model to the Edge, Putting Your Model into Production
This talk delves into the journey from model training to deployment at the edge – an often neglected yet vital aspect of machine learning implementation. It elucidates the essential practices and challenges associated with transitioning an AI model from a controlled environment to real-world edge devices.
Joy is a Machine learning Engineer at Secury360, a startup offers a hardware box that turns your security cameras into a perimeter security system with no false detections. He is responsible for the model training, active learning infrastructure and managing the labeling team. If you have been in the FiftyOne Slack you probably have seen him around.
Using PyTorch DDP and Kubeflow to Fine Tune Computer Vision and NLP Models on AWS
In this talk, we will explore the use of PyTorch Distributed Data Parallel (DDP) and the ‘gloo’ backend in combination with Kubeflow to run a distributed fine-tuning workload for foundational computer vision (CV) and natural language processing (NLP) models in the AWS cloud. We will focus on fine-tuning ResNet and GPT models using PyTorch and Intel’s Extension for PyTorch, with an emphasis on the benefits of hardware-level optimizations like AVX-512 and Intel AMX. By leveraging distributed training and these hardware optimizations, we can significantly reduce the time required to train these models, making them more efficient and effective. We will provide practical examples and guidelines for implementing this approach in real-world scenarios.
Eduardo Alvarez is a Senior AI Solutions Engineer at Intel and a specialist in applied deep learning and AI solution design. His background includes building software tools for the energy sector, and his primary interests lie in time-series analysis, computer vision, and cloud solutions architecture. Additionally, he is a community leader in data science and ML/AI for geosciences.