Machine Learning

Real-time data has unique traits that separate it from other types of data used in machine-learning models.

Artists, not their tools, make art.

OT has lagged in utilizing machine learning to harness the full scope of data.

Plus: Five trends that we believe will happen in the world of QA in the next three years.

"It is important to bridge the brownfield world to a new architecture leveraging open standards and broaden access to data."

any prediction model should first be built on first principal historical failures paired with supporting event/process data.

There are promising applications of dynamic scheduling, but further research is needed to scale and scope this methodology.

"The more that complex big-data applications migrate to the cloud, the greater the likelihood of misallocation of resources."

"Most organizations are often unable to scale and take full advantage of end-to-end automation."