Operations | Monitoring | ITSM | DevOps | Cloud

You’ve probably heard about neural networks being hailed as the next big step in technological advancements in artificial intelligence (AI). Beyond its often exaggerated depiction in fiction and media, neural networks have slowly but steadily become an invaluable asset in the IT world. It is under constant research in data science and computer science.

DevOps has made a hugely positive impact on the way organizations operate. In essence, it’s a way for different project stages and teams to work. Truth be told, when it comes to integrating various aspects of the IT R&D chain, it’s a godsend. On the other hand, Agile is about emphasizing continuous delivery in software development with a focus on iterative, incremental, and evolutionary development. Today, we’ll learn about DevOps vs.

On this occasion, we’ll talk about something pretty much apropos of our core concerns: Business Process Management System (BPMS) — and the software that lets you get it right. We know streamlining and optimizing operations and processes is very rewarding at the end of the day. Hence, this article will explain why you should care about process management and automation in general. First, let’s briefly examine what we mean by BPMS.

IT4IT is a relatively new entrant in ITSM frameworks and ITIL practices, with the first version released only in October 2015. The past decade has seen rapid innovation, and companies that could change and innovate quickly have had the edge over the rest. The framework arose out of a need to support rapid changes in organizations and, at the same time, manage risk and compliance. Read on to know more about this new standard and how it works.

Managed Service Providers or MSPs in asset management, which started as remote IT support and troubleshooting in the 90s, have evolved into one of the most prominent IT business models. According to a Statista’s report, the global MSP market share was valued at 152.02 billion USD in 2020 and is expected to grow close to 274 billion USD by 2026. Rather than just troubleshoot or monitor remote IT systems, MSPs these days offer services that manage the IT infrastructure for organizations entirely.

DevOps is not just a set of practices but a culture that focuses on collaboration. Thus, its primary goal is to foster improved communication between software development and operations teams. There’s, more or less, a perfect DevOps tool for every job. This article will cover the 10 best of them so you can make informed choices.

As a manager, it’s up to you to be on-point about who does what in your team. While all of the job titles and descriptions can get a little confusing at times, knowing comes with the territory. Nowadays, DevOps engineers are an increasingly important part of any solid IT team. Today, we’ll take a look at the DevOps engineer job description, so by the time we’re done, you’ll be an expert on the topic. DevOps, as we’ve covered before, is not one thing, but many.

Patch management best practices ensure we do patch deployment effectively and in time to prevent significant data losses, cybersecurity attacks, and other risks when the software is not updated. Patch management is an essential part of IT Asset Management. In short, it consists of distributing and applying updates or software patches. It should be a well-established and thorough process to handle any vulnerability and other issues. This article will discuss these best practices.

LDAP or Lightweight Directory Access Protocol is one of the oldest and most popular protocols used to retrieve information from directory services, authenticate users, and build applications that don’t compare on security or speed. It’s one of the protocols to manage assets and data over a network and provides secure access to them. So what is LDAP? How does it work? What are some of the best practices while using the LDAP protocol? Let’s have a look.

Machine learning has garnered a lot of attention in the past few years. The reason behind this might be the high amount of data from applications, the ever-increasing computational power, the development of better algorithms, and a deeper understanding of data science. We have already talked about artificial intelligence (AI) in a previous blog post. In this opportunity, we will learn about machine learning, what it is and how it works with examples and ITSM applications.