Push Technology announced the launch of a new Kafka Adapter for their Diffusion Intelligent Data Mesh.
In 2020, DevOps teams will face heightened expectations for higher speed and frequency of code delivery, which means their IT environments will become even more modular, ephemeral and dynamic — and significantly more complicated to monitor.
As a result, AIOps will further cement its position as the most effective technology that DevOps teams can use to see and control what's going on with their applications and their underlying infrastructure, so that they can prevent outages. Here I outline five key trends to watch related to how AIOps will impact DevOps in 2020 and beyond.
1. Better collaboration among DevOps teams
DevOps teams historically have been given free rein to operate independently. In fact, that freedom extends even to individuals within a single DevOps team. In 2020, many organizations will put their foot down and say "no more." They will enforce a more disciplined approach, preserving what's good in DevOps but imposing more external control.
AIOps will help with this effort by providing a holistic view and cross-team awareness of the overall performance of applications and the status of their infrastructure. AIOps ingests data from all DevOps monitoring tools, filters out event noise across the board, and correlates important alerts, pinpointing root causes across the CI/CD process. That way, silos among DevOps teams are eliminated, cross-team collaboration is enhanced, and application problems are detected early and resolved quickly.
2. DevOps automation
In DevOps there's a lot of automation focused on moving code from one stage to the next and injecting it into a production environment. In 2020, we'll see the automation of many aspects of code writing. This isn't about replicating existing lines of code. Rather, it relates to making decisions about flow and what operations to apply — effectively automating the coders' tasks.
With that urgency of automation, DevOps teams will have no choice but to trust AI more and more, as the "need for speed" drives the need for automation. DevOps teams will realize that observability-centric IT management focused on metrics, logs, and traces, and on the analysis of these data types in real time, will require AI and ML technologies, because those data sets are incredibly complex, dynamic, volatile and noisy.
To deal with those data sets, both development and operations teams require an analytical and diagnostic tool to predict what's going on. In this scenario, AIOps technologies become a necessity — without a smart filter DevOps loses control, which in turn slows down the development cycle.
3. New skills for DevOps staff
As DevOps teams further embrace AIOps, and AIOps in turn absorbs and automates a number of formerly manual tasks, DevOps team members will need new skills, and new jobs will emerge. For example, one such new job will be AIOps Architect, a developer of meta-algorithms for controlling AIOps platforms. To address this shift, DevOps pros need to ponder how to expand and renew their skills, and organizations must provide appropriate training. For example, data science will emerge as a critical skill, as well as a comprehensive understanding of modern IT systems.
4. AIOps as enabler of DevSecOps
The next evolution of DevOps involves meshing security into its processes, so that security checks are incorporated and automated throughout the CI/CD pipeline. AIOps will deepen that integration, and enhance the ability of DevOps teams to deal with development, operations and security-related problems through a single lens.
5. More transparency into AIOps algorithms
AI systems can make errors, which raises the concern of being able to audit effectively what AIOps systems do. There are certain types of algorithms that are very popular today that are intractable — literally a human being can't understand how they have arrived at their conclusions. As AIOps becomes more widely deployed, we'll see a move away from these black-box algorithms which you can't ever truly understand how they arrive at their results, and towards other types of AI algorithms that are more transparent to anyone — even to another IT system that's trying to audit their results.
As the stakes get higher for DevOps teams in 2020, I hope the five trends outlined in this article will help them understand the current and future impact AIOps will have on their careers, tools and processes. With applications and their infrastructures getting more and more complex to monitor, it's imperative for DevOps teams to understand how AIOps can provide full observability across the CI/CD cycle to quickly detect and fix issues before users are impacted.