Salvatore Salamone

Many businesses use container-based microservices architectures to develop AI. The container orchestrator Kubernetes can help with management and scaling.

Here is a quick overview of the major real-time analytics developments and innovations that got our readers’ attention over the last year.

Perhaps the underlying reason AI, CI, and other technologies will be disruptive factors in 2020 is digital transformation.

CI based on analysis of sensor and IoT data can help spot state changes in devices and make predictions about an asset’s failire probability.

In the news this week: chip vendors make acquisitions and introduce new AI products, an analytic database provider releases an enhanced solution, and more.

If businesses do not adopt their own best practices in addressing AI transparency and bias issues, they may not have a choice in the future.

In the news this week: enhancements to a mainstay DBMS, a merger between two enterprise AI companies, the next generation of a virtual beauty recommendation app, and more.

Today, there is a need to transition from simply analyzing streaming data to developing insights that understand events in real-time.

Some of the most innovative uses of smart mirrors with artificial intelligence can be found in the retail and automotive industries.

In the news this week: enhancements to an all-in-one database for AI applications, help for data migration from edge to cloud, and new functionality for advanced analytics on graph databases.

By some estimates, collecting, curating, and tagging data accounts for about 80% of the effort in modern AI projects.

The demand for AI-enabled applications that deliver increasingly refined results is driving the need for high-quality annotated data to train AI models.