Deci's YOLO-NAS architecture provides today's state of the art in Machine Vision, specifically the key task of Object Detection. Harpreet Sahota joins us from Deci today to detail YOLO-NAS as well as where Computer Vision is going next.

Harpreet:
• Leads the deep learning developer community at Deci AI, an Israeli startup that has raised over $55m in venture capital and that recently open-sourced the YOLO-NAS deep learning model architecture.
• Through prolific data science content creation, including The Artists of Data Science podcast and his LinkedIn live streams, Harpreet has amassed a social-media following in excess of 70,000 followers.
• Previously worked as a lead data scientist and as a biostatistician.
• Holds a master’s in mathematics and statistics from Illinois State University.

Today’s episode will likely appeal most to technical practitioners like data scientists, but we did our best to break down technical concepts so that anyone who’d like to understand the latest in machine vision can follow along.

In the episode, Harpreet details:
• What exactly object detection is.
• How object detection models are evaluated.
• How machine vision models have evolved to excel at object detection, with an emphasis on the modern deep learning approaches.
• How a “neural architecture search” algorithm enabled Deci to develop YOLO-NAS, an optimal object detection model architecture.
• The technical approaches that will enable large architectures like YOLO-NAS to be compute-efficient enough to run on edge devices.
• His “top-down” approach to learning deep learning, including his recommended learning path.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.