From Prototype to Production – Building Successful Computer Vision Models at Scale
Are you training a self-driving car, detecting animals with drones, monitoring equipment for predictive maintenance, or identifying car damage for insurance claims? The challenges to effectively train, deploy & tune a computer vision model at scale remain the same. In this session, we will talk about what the most innovative companies are doing with Computer Vision. We will share with you real world techniques and tooling for managing AI at scale including but not limited to: • Key success factors when scoping an ML project • Identifying and sourcing your training data • Picking the right data annotation tools & processes • Ensuring training data variability (poses, illumination, intra class variability and occlusion) • Autoscaling training data-creation and model-building
Session ID: Presentation Type: On-Demand Session (Recorded)
Date / Time: [Content On-Demand] @ On demand ET (US)
Presented by:Appen collects and labels images, text, speech, audio, video, and other data used to build and continuously improve the world’s most innovative artificial intelligence systems. Click for more details and additional sessions and content.
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