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.

With the complex and steadfast growth of IT service delivery processes, organizations and their internal teams have come to rely on several tools in their toolbox to deliver best-in-class products and services. The use of AIOps, AI/ML, and overall automation has shaped modern delivery methods, but what we call this process, and how we grow to advance it, has yet to find a definition that’s universally recognized.

What is the future of software development and deployment? DevOps processes have helped take developers and operations folks out of their silos and share responsibilities. But is it enough to succeed long term? Many companies have yet to embrace DevOps completely across their teams. Clearly, the culture of sharing tools, a key aspect of DevOps, is not enough.