Software is a mixture. We can liken enterprise software application development to the process of making soup i.e. there is plenty of scope for experimentation and the introduction of new ingredients or techniques, but there are also recipes for how to do it right. Indeed, a well-known brand of technical

In the age of rapid technological advancement, terms like Machine Learning (ML) and Deep Learning (DL) frequently surface in conversations around innovation, automation, and artificial intelligence. While they both fall under the broad umbrella of AI, they are not synonymous. This blog aims to elucidate the differences between ML and

Revealing the future: A Full performance evaluation of Quantum vs Classical Machine Learning The goal of the research field known as quantum ML (QML) is to include quantum algorithms in machine learning initiatives. It improves computer performance and data storage by utilizing the capabilities of quantum computing and quantum physics, frequently combining both classical

AI networking overview Artificial intelligence (AI) networking is an evolution of AIOps (AI for IT operations) that focuses on ongoing “day 2” management, maintenance and optimization of a network. It combines AI with networking infrastructure to automate and optimize IT operations. Where AIOps has a broader focus on the information

We are witnessing the increased use of IoT sensors, even in traditional sectors such as healthcare. Be it smart factories or cities, supply chains, connected homes, or cars, this sensor network deployed for these use cases takes immediate action based on incoming data. However, first transmitting data to the cloud and