Previous columns in this series introduced the problem of data protection in machine learning (ML), emphasizing the real challenge that operational query data pose. That is, when you use an ML system, ...

Machine learning is often key to success for today’s institutions that rely heavily on data. But often, data science teams can have a difficult time convincing their organizations of the breadth and ...

Software engineers are increasingly seeking structured pathways to transition into machine learning roles as companies expand ...

Alegion is in the business of preparing machine learning training datasets for Fortune 1000 organizations. When the team at Alegion first meets with a prospective client, it's pretty predictable. Most ...

eWeek content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More Machine learning (ML) uses advanced mathematical models ...

As machine learning becomes more pervasive in the data center and the cloud there will be a need to share and aggregate information and knowledge but without exposing or moving the underlying data.

When people hear “artificial intelligence,” many envision “big data.” There’s a reason for that: some of the most prominent AI breakthroughs in the past decade have relied on enormous data sets. Image ...

From exploratory data analysis to automated machine learning, look to these techniques to get your data science project moving — and to build better models. Do you need to classify data or predict ...

Balance training patients may soon be able to get AI feedback during home exercises, with four wearable sensors and a new machine learning model developed at the University of Michigan.