Salvatore Salamone

A hybrid cloud approach lets businesses essentially pick and choose the various aspects of their analytics and ML workloads that they want to keep on-premises and which they want to run in the cloud.

In this week’s real-time analytics news: The Linux Foundation and the World Bank released a free, online quantum computing training course to provide an understanding of how the technology could be used for complex decision making.

IBM’s Matt Roberts discusses the factors driving the demand for integration, common obstacles to success, and how the right integration technologies complemented with AI can help.

Organizations need an easy, scalable, and secure way to integrate many data sources across hybrid environments. A suitable solution must support enterprise messaging, event streaming, high-speed data transfer, and more.

Companies will be hard-pressed to deal with APIs that change or are discontinued over time. All the more need for API lifecycle management.

Connected products allow OEMs to move from equipment makers with one customer touch point at the initial sale to a more dynamic role over the long term as an interactive partner with their customers via an EaaS model of operations.

In this Q&A interview, Aible and Intel provide insights not only on how to avoid AI project failures but how they deliver guaranteed results from AI within 30 days.

In this week’s real-time analytics news: Cloudera announced the launch of Cloudera Data Platform (CDP) One, an all-in-one data lakehouse SaaS offering for self-service analytics and exploratory data science on any type of data.

Using analytics and AI, OEMs of connected equipment can gain a deep understanding of the status of a machine and move to a predictive maintenance mode of operation.

Hybrid cloud deployments are the new norm, allowing workloads and data to run and reside on a combination of on-premises systems, private clouds, and public cloud services.

In this week’s real-time analytics news: The NSF and Amazon awarded about a dozen organizations $9.5 million for projects that seek to root out unfairness and bias in AI and ML.

A new research collaboration will focus on Superlearners, a foundational machine learning technology that enables autonomy for all building applications.