Explore a comprehensive 41-minute talk on building scalable AI computer vision applications. Learn about standardized processes and workflows to accelerate development for both application developers and data scientists. Discover techniques for preparing datasets, efficient annotation, model selection, benchmarking, and deployment considerations. Gain insights into extracting poor-performing data, monitoring model quality, and understanding key metrics for computer vision applications. Delve into the challenges of production deployment and learn how to leverage recent advancements in AI and machine learning for various computer vision use cases.
Building Scalable AI Computer Vision Applications
Overview
Syllabus
Introduction
Questions
Who am I
Advancements
Format of Data
Data Generation
Annotation
Model Selection
Optimization
Benchmarking
Deployment
Monitoring
Summary
Taught by
Open Data Science
