Articles

Monitoring the infrastructure, apps and services, as well as logging the events for later analysis accounts for nearly half the effort of DevOps workflow. These 5 tools fit best for that purpose.

In the third part of our Deep Learning summary for 2017, we discuss the new discoveries and breakthroughs in reinforced learning and other fields.

Deep Learning is disrupting many industries, and yours might not be an exception. Learn of the most notable deep learning projects of 2017 and ride the wave, or risk being rolled over…

While DevOps is not purely a set of tools and practices and rather is a mindset and an approach to software delivery, using the must-have DevOps tools makes the workflow much better.

Artificial Intelligence (or AI) is the goal many researchers try to reach. Many people consider Deep Learning (DL) as a synonym of AI, while it is not so. Learn how Deep Learning works!

Until recently, Kubernetes did not have the native support for load balancing for the bare metal clusters. MetalLB is the new solution, currently in alpha version, aiming to close that gap.

Nearly every business requires some kind of IT services to run smoothly and succeed nowadays. While some things can be done yourself, there is only so much one man can do.

MongoDB is a widely popular database, yet at the moment AWS functionality lacks the way to configure simple backups for it. IT Svit team presents a solution to automate backups.

Big Data analysis is an essential tool for Business Intelligence, and Natural Language Processing (NLP) tools help process a flow of unstructured data from disparate sources.

We think Kubernetes might be one of the major tools in daily operations of any DevOps specialist, so knowing of the latest Kubernetes features and general approaches is quite useful.