Data quality is clearly paramount for a successful AI project, but how difficult is it to achieve?
Data Workforce
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.
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.
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 ...
Deep learning platform MissingLink and the MIT Media Lab brought together AI innovators in Boston last month for a meetup about how AI is changing the world for the better.
Your choices about tooling and workforce will be important factors in your success as you design, test, validate, and deploy any ML model.
Crowdsourcing seems to offer a cheap option for training machine learning models, but it’s rarely as inexpensive as it seems. Here are some of the hidden costs of the crowd.
