Production AI teams spend roughly 80% of their time building and maintaining training data infrastructure. Data science teams shouldn’t have to build their own expensive and incomplete tools for managing their data. They need a platform that acts as a central hub for creating and managing training data with internal or external labeling teams. Better ways to input and manage data result in higher-quality training data and more accurate machine learning (ML) models.
Teams need to be able to seamlessly manage, annotate, and iterate training data for production AI. ML engineers and labelers need a fast, powerful, and intuitive solution that gives them full visibility into the real-time operations of labelers and the quality and accuracy of labels.
Our machine learning training data solution
NetApp and Labelbox have partnered to deliver an integrated training data solution that is streamlined and creates new productivity and efficiency metrics. Data scientists rely heavily on iterating on training data, but they need a central place to store and house all of their organization’s training data. With the NetApp® ONTAP® AI proven architecture, you can fully realize the promise of AI and deep learning (DL) by simplifying, accelerating, and integrating your data pipeline. With Labelbox, the same datasets can be reused multiple times with less effort.
The joint solution works both in the cloud and on premises, and you can search, browse, and curate all of your training data in one place. Using a training data solution from Labelbox and NetApp offers many benefits, such as productivity gains with feature-level analytics and a streamlined design to enable faster iteration cycles. Sharing and collaboration are improved with advanced workflows across distributed labeling teams, and automated task distribution, team provisioning, and dataset management increase collaboration across business and technical teams. And companies save money by not having to build in-house labeling systems that are brittle, hard to maintain, and often lack the features that are required to scale.
For more information, visit NetApp.com/ai.