Computer Vision today has been adopted by many firms - from major MNC's to start-ups building their entire product around Machine Vision. We've worked with over 50+ companies and compiled our observations in this guide about several key challenges they've faced on their quest to develop robust Computer Vision models while providing best practices for data-centric development. In this guide, you’ll discover:

• Model-Centric vs Data-Centric AI Development
• Common challenges faced by Computer Vision Teams
• Our Iterative Cycle of Data-Centric Development
• Processes, standards, and tools that enable the creation of a high-quality training dataset and model workflow
• How teams can troubleshoot and iterate on their models to address edge cases and model confusion