Not having to move data to different databases can enable robust functionality for many enterprises. It is a model exemplified by the smartphone, which served as a catalyst for the app-based economy.

Companies become so good at generating perfect fake customers that they stop talking to real ones. The models work great until they meet actual humans.

Regardless of what data model (s) you choose to bring into your company’s data strategy, it’s important to have the right people and processes in place to make these models work.

Data labeling helps to select relevant data. As the saying goes, “GIGO: garbage in, garbage out.” Good data and labels lead to good results. - Filip Dvorak, Filuta AI 9.

Fractured and incomplete datasets are a key barrier towards effectively training AI models for deployment in healthcare ...

According to DAMA International’s Guide to the Data Management Body of Knowledge, enterprise data architecture typically consists of three major sets of design components. First, an enterprise data ...

Confidentiality, to a lesser extent, and integrity, to the greatest extent, are the most important considerations with AI ...

The Readiness Decision Impact Model, in development at the Defense Department’s readiness office, can take a question such as, “How will extending a squadron’s deployment now affect its ...

Companies like Scale AI and Surge have proven there’s a market for human-labeled data, like professionals’ answers to complex math or law questions, that AI research labs can use to train their models ...

HARRISBURG — A Pennsylvania database of police personnel records lauded as a national model is riddled with loopholes that raise serious questions about its ability to flag police officers with ...