machine learning training data solutionProduction 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.

NetApp ONTAP AI proven architecture

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.

Mike McNamara

Mike McNamara is a senior leader of product and solution marketing at NetApp with 25 years of data management and data storage marketing experience. Before joining NetApp over 10 years ago, Mike worked at Adaptec, EMC and HP. Mike was a key team leader driving the launch of the industry’s first cloud-connected AI/ML solution (NetApp), unified scale-out and hybrid cloud storage system and software (NetApp), iSCSI and SAS storage system and software (Adaptec), and Fibre Channel storage system (EMC CLARiiON). In addition to his past role as marketing chairperson for the Fibre Channel Industry Association, he is a member of the Ethernet Technology Summit Conference Advisory Board, a member of the Ethernet Alliance, a regular contributor to industry journals, and a frequent speaker at events. Mike also published a book through FriesenPress titled "Scale-Out Storage - The Next Frontier in Enterprise Data Management", and was listed as a top 50 B2B product marketer to watch by Kapos.

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