According to Ashley Kramer, Alteryx's VP of Product Management, Promote will address this gap by allowing deployment of models, and generation of REST APIs around them, all of which can be invoked ...

We’ve gotten pretty good at building machine learning models. From legacy platforms like SAS to modern MPP databases and Hadoop clusters, if you want to train up regression or classification models, ...

Oracle, a company not exactly known for having the best relationship with the open source community, is releasing a new open source tool today called Graphpipe, which is designed to simplify and ...

In this special guest feature, Neil Cohen, Vice President at Edge Intelligence, examines the question: where should businesses develop and execute machine learning? This article explores the pros and ...

We called it Machine Learning October Fest. Last week saw the nearly synchronized breakout of a number of news centered around machine learning (ML): The release of PyTorch 1.0 beta from Facebook, ...

With so many machine learning projects failing to launch – never achieving model deployment – the ML team has got to do everything in their power to anticipate any impediments to model ...

Implementing artificial intelligence (AI) at the edge requires a tradeoff between cost and performance in processing and considerable development effort. Throwing in machine learning to make the edge ...

ParallelM, a provider of machine learning operationalization (MLOps) software, has released a new version of MCenter that includes REST-based serving using Kubernetes to create a no-code, autoscaling ...

OctoML is charging ahead with its machine learning deployment software and on Friday announced a $15 million investment round to help support growth. The Seattle startup spun out of the University of ...