DevOps’s concept and development have significantly changed how IT teams work in the last decade. Small and large teams alike can see the difference when they switch from traditional software development cycles to a DevOps cycle: However, effectively embracing DevOps takes work. Thankfully, there are many ways to navigate this challenging journey, and this article will explore the four most effective ones.

A DevOps Self-Assessment provides 15 questions about your DevOps processes and practices and ranks the maturity of your DevOps initiative. Achieving better business outcomes hinges on the ability to release software faster and provide responsive support. DevOps maturity assessments play a critical role in this process by helping organizations pinpoint inefficiencies, identify gaps in collaboration, and refine their workflows.

Smooth and efficient software deployment is critical to delivering high-quality applications that meet user expectations. Still, many software failures can be traced back to deployment issues. A well-structured deployment strategy can help DevOps & SREs teams prevent these errors, ensure system reliability, and enhance user satisfaction. This guide explores software deployment best practices, from planning and execution to post-deployment monitoring and incident management.

Ensuring your organization can continue running critical services, even during unexpected challenges, requires a solid IT resilience plan. An IT resilience plan involves more than just traditional disaster recovery. It focuses on keeping vital applications, data, and business operations intact no matter what happens. In this guide, we’ll explore key components and best practices to help you establish a comprehensive plan for ongoing business continuity.

Data security and DevSecOps should be top priorities for every business, but some of us may fear the complexities of implementation. Many organizations are still shelving security concerns in favor of quick IT upgrades and software development. Security is no longer optional. Changes to the laws that govern the collection and use of personal information have forced many to prioritize security sooner rather than later.

Artificial intelligence (AI) is reshaping industries, and software development is no exception. By integrating AI technologies like machine learning, generative AI, and natural language processing, development teams can optimize workflows, enhance code quality, and reduce time-to-market. In this article, we’ll examine AI in software development, including its benefits, challenges, and most recent developments. Let’s get started.

More than ever, DevOps teams are constantly tasked with improving collaboration, accelerating software development, and ensuring smooth operations. However, traditional monitoring and alerting methods, often called a “black box approach,” offer limited insight into system performance. As a result, teams rely on reactive approaches, only responding to incidents after they occur without prior planning or strategy.

Mean time to repair (MTTR), sometimes referred to as mean time to resolution, is a popular DevOps and site reliability engineering (SRE) team metric. MTTR identifies the overall availability and disaster recovery aspects of your IT assets or application workloads. The acronym MTTR can cause some confusion since it has different meanings across different industries. Sometimes, MTTR refers to mean time to respond: the amount of time needed to react to a problem.

Observability refers to the ability of the DevOps team to track, monitor, and measure the state of their pipeline and operations. Without observability, you are working in the dark, unaware of what is working. With the growing complexity of modern IT systems, DevOps observability is no longer optional. Gartner estimates that by 2026, 50% of enterprises implementing distributed data architectures will have adopted data observability tools, up from less than 20% in 2024.

We often hear about the many benefits AIOps (Artificial Intelligence for IT Operations) brings to businesses. But how can you develop an effective AIOps strategy? Where do you even start? What are the best practices or implementation challenges? These and many more questions must be answered before beginning your AIOps journey. In this guide, we will explore the steps for creating an effective AIOps strategy and discuss crucial components, obstacles, and best practices for successful implementation.