New Relic, following its acquisition of SignifAI, is in the process of accelerating its investments in machine learning algorithms and other forms of artificial intelligence (AI) as part of an effort to make it easier to separate the signal from the noise created by all the alerts in an IT environment.
Aaron Johnson, senior vice president of product management at New Relic, said SignifAI represents an opportunity for New Relic to create a superset platform that can apply advanced analytics to a much wider range of data sources. SignifAI has already created more than 60 integrations spanning open source and commercial monitoring tools that are widely used across DevOps toolchains, he noted, adding that as New Relic moves to add other data sources, there’s an opportunity for the company to distinguish itself beyond providing software-as-a-service (SaaS) tools for monitoring applications and infrastructure.
It’s obviously still early days for AI in the realm of DevOps. But it’s also clear that with the rise of microservices and containers, soon it will be impracticable for IT organizations to manually manage their IT environments. Various forms of automation based on AIOps platforms infused with machine and deep learning algorithms will be required. As a provider of SaaS-based tools for monitoring applications and infrastructure, New Relic already has access to vast amounts of data required to train AI models on optimizing a specific IT environment.
Of course, there’s lots of competition across the IT monitoring category, so it remains to be seen which vendors will have the resources required to drive continuing investments in AIOps.
In the meantime, DevOps teams will need to adjust their processes to take advantage of AIOps. It’s unlikely AIOps platforms will eliminate the need for DevOps teams, but the number of applications and platforms any DevOps team must manage effectively will increase substantially. Rather than replacing humans, AIOps platforms will reduce the pressure to hire additional IT staff when professionals with DevOps skills are already in short supply. The AIOps platform, for all intents and purposes, acts like a co-pilot.
The biggest issue IT organizations are likely to encounter along that journey is establishing trust with any AIOps platform they implement. It takes a while for most AI models to learn the IT environment before that platform makes any reliable recommendations. In most cases, DevOps teams will have to dedicate staff to train and update AI models continuously as the IT environment changes. It may take years, however, before DevOps teams are comfortable enough to move beyond trusting a recommendation and enable an AIOps platform to automate processes on its own. After all, the only thing worse about being wrong about any IT decision is being wrong about it at scale.
AI at this point is all but inevitable in DevOps environments. But it doesn’t necessarily follow that just because something is based on AI it always will be smarter than experienced DevOps professionals.