The case for training data platforms

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:

1. Build their own labeling solution
2. Outsource labeling
3. Use a training data platform (TDP) like Labelbox

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:

1. Annotate with an interface that’s accessible to both experienced labelers and domain experts. Our TDP also enables automated labeling workflows
2. Manage all your data, people, and processes with a central system of record
3. Iterate faster on training data to accelerate the model training process

Read AI Needs an Open Labeling Platform for more details on why a TDP is the best choice for enterprise AI teams.