What's New

Compute analytics in financial services have evolved over the past decade. Popular tools for forecasting and assessing risk like statistical functions involving linear regression, logistic regression have given way to more sophisticated models. Classic decision tree or regression tree algorithms have evolved to modern variations like random forest techniques and gradient boosted trees. Fraud detection…

Today, we’re excited to unveil Kinetica database v6.0.1, which bundles TensorFlow™, the industry’s leading open-source library for machine intelligence, to bring distributed deep learning to the enterprise. TensorFlow is rapidly becoming the go-to open-source library for machine intelligence and deep learning. But as businesses seek to harness the value and opportunities that deep learning offers,…

Machine learning (ML) has become one of the hottest areas in data, with computational systems now able to learn patterns in data and act on that information. The applications are wide-ranging: from autonomous robots, to image recognition, drug discovery, fraud detection, etc. At the cutting edge is deep learning, which draws its inspiration from the…

This week, over 500 technologists – from enterprises such as BNY Mellon, Goldman Sachs, Google, ING, Target and many more – came together at Kafka Summit to learn and share tips in working with and getting value from high-speed data. At the event it was evident there is growing interest in instantaneous insights, and how…

Financial institutions have long been on the cutting edge of quantitative analytics. Trade decisioning, risk calculations, and fraud prevention are all now heavily driven by data. But as the volume of data has grown, as analysis has become ever more sophisticated, and as pressure builds for timely results, computation is more and more of a…