Data quality is clearly paramount for a successful AI project, but how difficult is it to achieve?
CloudFactory Blog
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.
CloudFactory partner Scientia shares the AI opportunity and the importance of quality data for machine learning.
Mark Sears, founder and CEO, shares how CloudFactory will use its latest round of funding, a Series-C round led by FTV Capital.
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.
Azavea's mission is to create advanced geospatial technology and research for civic and social impact. They interviewed a handful of leading data labeling firms, and studiously ...
Gartner predicts 85% of AI projects will fail. One of the leading reasons is low-quality data labeling. High-performing machine learning algorithms require high-quality data. ...
When you have massive data to label for machine learning, it makes sense to outsource it. But what happens when your data is sensitive, protected, or private? Here’s a quick ...
