OpsRamp has advanced its case for AIOps via a Fall update to its software-as-a-service (SaaS) applications that adds a Topology Explorer alongside an enhanced set of Service Maps.
In addition, OpsRamp added a cloud database monitoring capabilities for relational databases deployed on public clouds including Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP).
Mahesh Ramachandran, vice president of product management for OpsRamp, said the Topology Explorer makes it possible to automate network and application discovery to accelerate impact analysis and troubleshooting. An existing Service Map capability, meanwhile, has been enhanced to allow DevOps teams to invoke visualizations within an alert to better prioritize responses.
OpsRamp has also added a pause capability for alerts, intended to be used during maintenance windows to prevent alert floods. Alert filters now include text-based search that, when combined with the AI Inference Engine, helps resolve issues faster. DevOps teams also can create policies to notify them of alert state changes.
Finally, OpsRamp has added reporting tools that enable DevOps teams to determine not just the amount of resources being consumed on a public cloud, but also which public cloud or on-premises environment is generating the most trouble for the IT organization.
In general, Ramachandran said most organizations have very little way in the way of tools that enable them to realistically compare how one platform performs over another when running specific classes of workloads. By leveraging the machine learning algorithms that OpsRamp includes in its SaaS applications, enough data becomes available to provide DevOps teams with actionable insights, he said.
AI tools typically require access to massive amounts of data. By making available IT operations tools available as a cloud service, OpsRamp is trying to make it practical for DevOps teams to employ AI on both tactical and strategic levels without requiring an IT team to acquire special data science skills, Ramachandran said, adding such an approach also provides the added benefit of making it possible to holistically manage IT silos across multiple classes of infrastructure and applications.
IT organizations are still in the early stages of coming to terms with AIOps and its potential impact on DevOps processes. Many of the manual processes that tend to inhibit widespread adoption of DevOps are clearly going to be automated. In fact, many of those tasks will be accomplished using voice commands that bots will be able to understand. In other cases, the bots will inform DevOps teams when an issue needs to be addressed long before they would otherwise be aware of it. Less clear is how job roles within DevOps teams will evolve as it becomes easier to manage applications and infrastructure comprehensively.
Of course, the rate that transition will occur will vary widely by organization. But rather than debating the right approach to automation, it’s becoming increasingly apparent that many of those decisions will be made by machines. The unspoken assumption being made is that humans responsible for managing IT will be comfortable that the right decisions are being made on their behalf.
