Edge computingis the concept of capturing and processing data as close to its source or end user as possible. Typically, the data source is an internet of things (IoT) sensor. The processing is done locally by
“Currently, around 10% of enterprise-generated data is created and processed outside a traditional centralized data center or cloud,” says Rao. "By 2025, Gartner predicts this figure will reach 75%." Use cases Edge computing solutions can take many forms. They can be mobile in a vehic...
Apache Sparkis a free and open-source cluster-computing system created to process and analyze big data on a distributed computing system (a cluster). Along with the Python, Scala, and Java APIs, which expose principles of distributed computing, they are useful for developers who work on larger...
no one has given a straight answer about exactly what it is they’re building. (A quick side note: This piece focuses on the AI debate in the US and Europe, largely because many of the best-funded, most cutting-edge AI labs are there. But of course there’s important research...
What is Data in Computing? Data is information that can be interpreted and used by computers. It is a collection of facts, such as numbers, words, measurements, observations or even just descriptions of things. In computing, data is typically stored electronically in the form of files or data...
Modern machine learning has its roots in Boolean logic. George Boole came up with a kind of algebra in which all values could be reduced to binary values. As a result, the binary systems modern computing is based on can be applied to complex, nuanced things. ...
Numeric numbers, also known as “numerals” or “digits”, are the symbols we use to represent numbers in computing and mathematics. They range from 0 to 9 and can be combined to create larger values (i.e 123 is composed of three numeric components: 1, 2 and 3). In addition to regul...
of AI models consume large amounts of energy and water. Consequently, training and running AI models has asignificant impact on the climate. AI's carbon footprint is especially concerning for large generative models, which require a great deal of computing resources for training and ongoing use....
4. Cloud and Edge Computing Integration The future of DevOps will be greatly influenced by cloud computing as well as edge computing. To cope with the shift towards cloud applications, which are becoming more important at the edge level, new forms of DevOps have to be developed. Using cloud...
Generative AI can be applied in an array of use cases across industries to generate content, summarize complex information and streamline various enterprise processes. The technology is becoming more accessible to users of all kinds, thanks to cutting-edgebreakthroughs like GPT, diffusion models and ...