Maximizing Model Accuracy With Data-centric AI Practices скачать в хорошем качестве

Maximizing Model Accuracy With Data-centric AI Practices

In computer vision projects, managing data is often an afterthought. That is why at least 31% of projects fail. In this webinar, the speaker will talk about how to build a solid data foundation to train more accurate and cost-effective computer vision models. In this session, you will learn about Hub (on Github https://github.com/activeloopai/Hub), an open-source dataset format for AI. Hub works with computer vision datasets of any size and enables easy creation, storage, version control, and streaming to ML frameworks while training. Moreover, you will learn how to apply the data-centric framework to resolve common data bottlenecks when using tools like Amazon SageMaker. The speaker will also demonstrate how to visualize and explore datasets – from MNIST to ImageNet. As a result of the session, you will be able to easily build computer vision data pipelines and fully utilize the compute resources. The talk is sponsored by Activeloop (https://www.activeloop.ai/) Speaker: Davit Buniatyan (Activeloop) https://www.aicamp.ai/event/eventdeta...