Operations | Monitoring | ITSM | DevOps | Cloud

Machine learning affects nearly every aspect of our daily lives. To understand how this technology works and how you can use machine learning, it’s necessary to know the difference between unsupervised and supervised machine learning. The following are essential points regarding the different aspects of unsupervised and supervised machine learning.

Nowadays, when most people think about the term “machine learning,” they think of advanced, refined applications such as Chat-GPT, the chatbot-based deep learning text generator, or AlphaGo, the computer program that’s currently the “world's best player” of the board game Go.

The Bleemeo Community Edition is the Open Source and self hosted version of the Bleemeo monitoring solution. It is composed of three main components: And some third party components: In this article, we will show you how to install and configure these components to have a fully working monitoring infrastructure.

A mock server can prove useful in many circumstances. Imagine you’re an engineer working on optimizing a feature inside of an existing API that relies on multiple other microservices to function properly. To fully test the optimizations, you’ll have to set up test versions of all the dependencies, which quickly proves to be quite a task in and of itself. This is where a mocks—a server that simulates the behavior of a real server—can be very beneficial.

Mobile learning consists of learning on the go, via your smartphone or tablet. In a business setting, mobile learning offers the possibility for employees to follow their training wherever they are, with online courses and other content being accessible at all times. This learning method is available on many learning management system (LMS) platforms but, as we will see later, it is necessary to be vigilant in the choice of the latter because mobile learning comes with some technical constraints.

The use of APIs is at the forefront of automation. There are many ways for developers and applications to make use of APIs, and GraphQL is one way to make the most of the data. As technologies continue to grow and evolve, especially with drivers like the DevOps methodology, it is important to keep on top of better and quicker ways to meet our business goals. This is one of the reasons I find GraphQL to be so exciting, and why everyone should understand it.

Artificial Intelligence (AI) - or more specifically Machine Learning (ML) - and automation were big topics for many of our customers in 2022. Common reasons for the interest in AI and automation were to: increase efficiency, reduce manual processing, minimise human error and - especially for the use of ML - identify ‘unknown unknowns’.