Edge Computing Is the Road to Customer Experience Nirvana
As devices create massive volumes of data, edge computing will allow brands to react to customer engagement at lightning speed. A Gartner report estimated that
As devices create massive volumes of data, edge computing will allow brands to react to customer engagement at lightning speed. A Gartner report estimated that
Imagine that all people around the world could use voice AI systems such as Alexa in their native tongues. One promising approach to realizing this
It’s no coincidence that deep learning became popular in the AI community following the rise of big data, since neural networks require huge amounts of
Over the past few years the “modern data stack” has entered the vernacular of the data world, describing a standardized, cloud-based data and analytics environment
For over a decade we have witnessed a proliferation of internet-connected devices. Nowadays, the number of internet-connected devices is estimated to be around 25 billion.
IoT product development crosses several domains of expertise from embedded design to communication protocols and cloud computing. Because of this complexity “end-to-end” or “edge-to-cloud” IoT
Thenext-generation wireless connectivity 5G has touched the ground to fuel artificial intelligence (AI) with its much-needed data power. Coupled with the Internet of Things (IoT),
In a world saturated by artificial intelligence, machine learning, and over-zealous talks about both, it is important to understand and identify the types of machine
Kubernetes is a production-grade container orchestration system, which automates the deployment, scaling and management of containerized applications. The project is open-sourced and battle-tested with mission-critical
For many people who are not versed in the intricacies of IT and technological infrastructures, smart cities seem like a concept from science fiction. Could
As we enter 2020, companies will have to address new and persistent digital challenges that affect system and business performance. How are companies coping with
There are two fundamentally different and complementary ways of accelerating machine learning workloads: 2. By horizontal scaling or scaling-out, where one adds more nodes to