Machine Learning

Applying machine learning algorithms to your business process can be quite beneficial: whether it is to predict incoming workloads to better allocate internal and external resources, or to segment customer groups to personalize marketing campaigns, machine learning is the key to success.

A brief overview of Natural Computing with some hints as to how some of the research can be used today to enhance the way we build computer-based systems.

What are Cloudlets, what are it's benefits and why we need Cloudlets?

In this article, I will show how to use ML.Net (Machine Learning framework for .Net developers) to create an API service that will analyze comments sentiments (e.g. positive or negative sentiment of the comment).

Everyone has had an experience where we try to use our voice assistants on our phones and other devices only to have it respond with the most quirky, random things or otherwise not even try.

In this post I will briefly try to explain the machine learning process and why it calls for a different approach and mindset.

During my last conference, I shared my vision on SmartCity and mobility, taking the example of a duck, ordering a flying cab… in 2029

Take a look at the business scenarios when an organization should think of adopting the microservices design over monolithic applications.

What will be the next big technologies that will transform our lives 10 years into the future? How will these technologies be tested? Is testing even a thing anymore in 2030?

In the contemporary digital era, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as the two pillars of data science that are elusive to the common masses.

The term Quantum Computing sparks imagination about the limits of speed and the ability to simulate, optimize and predict future events. The scope of Quantum Computing inspires the marvel-like awe and anticipation as to when Marvel superhero Doctor Strange was observing future possibilities trying to predict all the different ways to defeat arguably their greatest enemy.

Terminology may seem like a trivial subject, but there have been and will continue to be a lot of misunderstandings when people use conflicting terminology. There is one example in the subject of machine learning which often is the base for misunderstandings. This concerns the term test data.