Data Workforce

Some organizations are winning in a pandemic by adapting to changing customer and market demands. We’ve provided adaptive workforces for a decade. Here’s what they look like.

Data quality is clearly paramount for a successful AI project, but how difficult is it to achieve?

Achieving a high level of accuracy in data labeling is vital. This concept can be understood if we think about a mural of Rubik’s Cubes®.

Any problem (like a Rubik’s cube®) is solvable with a documented process.

How solving a Rubik’s cube® is like labeling your unstructured data.

Melody Ayeli, who reviews AI projects for Toyota’s CIO, shared insights on common AI failure points in a session at AI Summit in San Francisco.

Our latest SlideShare provides details on your workforce options and shares best practices for choosing your AI workforce.