Labelbox•February 2, 2021
In a new VentureBeat article, AWS Principal Matt Asay delivers a compelling argument for enterprises adopting AI projects. Organizations serious about building AI have three choices to tackle the significant challenges of training data creation:
Building an in-house labeling system is a great solution if you’re Google or Facebook, but if your budget doesn’t extend quite so far, your team might end up with a collection of bespoke and open-source tools that end up being more work than their worth. Taking the time to build a production-class labeling system might also set your AI projects back by years.
Many enterprises outsource labeling. Unless you’re training a model on a publicly available dataset, however, this could be a risky choice. Your data might end up helping a direct competitor who uses the same labeling service — and you’ll probably still need to build your own internal tooling.
A training data platform that provides both labeling tools and workflows for collaboration, management, and iteration is the best answer for enterprise AI teams. Labelbox enables our customers to:
Read AI Needs an Open Labeling Platform for more details on why a TDP is the best choice for enterprise AI teams.
Labelbox•June 24, 2022
6 key best practices for AI teams to save time when creating AI data
Wading through a vast amount of unstructured data to accurately annotate assets requires a tremendous amount of patience, organization, and time. Learn six key time-saving practices for ML teams to implement when handling AI data.