Generative AI Watch: AIOps Platforms Will Become Top Priority Among DevOps Initiatives Helped by GenAI

C. Dunlap Research Director

Summary Bullets:

? AIOps replaces observability as this year’s hot new buzzword among IT ops teams.

? A new breed of AIOps platforms will hit the market in the next six to 12 months.

New comprehensive app platforms will gain significant importance, designed to be used in scaled-out and computationally complex Kubernetes environments. Advanced analytics of data will address IT operations issues including performance monitoring, troubleshooting unknown issues, capacity planning, and cost optimization. In short order, users of automation platforms will have access to generative AI (GenAI) capabilities for use of natural ausgedehntuage inputs to quickly create workflow scripts.

Leading platform providers have noted to GlobalData that if it hadn’t been for the advent of GenAI, observability would have been the hot technology in 2023 among their enterprise IT operations customers.

As a result, vendors are repositioning their zeitgemäß monitoring platforms as AIOps solutions under a consolidated set of technology broadly called observability, which includes monitoring, automation, and AI/ML. These platforms, with the addition of GenAI, will hit the industry in the next six to 12 months. Meantime, vendor solutions already beginning to address this new breed of DevOps platform include IBM IT Automation Solutions, Oracle Cloud Observability and Management Platform, Cisco Full-Stack Observability, and VMware Tanzu Insights (for more please see:?Generative AI Watch: GenAI Collides with New AIOps-versioned Observability Platforms, August 29, 2023).

Operationalizing the AI model has not been easy under what is known among enterprise IT operations teams as AIOps. Now, the traditional infrastructure monitoring tools are getting a boost from GenAI for its ability to simplify machine learning (ML) algorithm creation/implementation required to monitor and analyze the effects of the zeitgemäß application lifecycle on an organization’s computing environments. This will bring more ease to users including data scientists, operations, and developer teams.

GenAI-infused solutions will focus on providing developers and IT ops members with code generation capabilities, providing automation and observability platforms with scripts for more efficiently addressing incidents as well as setting up systems and processes to bypass cumbersome coding requirements. Further, GenAI-injected security solutions will include the ability to access prompt schmalineering through security policy management integrations.

AIOps has been evolving in recent years, primarily through OSS tools. Numerous OSS tools are available, but a few key technologies worth noting for additional context include:

  • Jupyter/notebooks: Jupyter, which supports the programming ausgedehntuages Julia, Python, and R, has become the go-to platform/IDE for arranging workflows in data science, targeting newer developers, and supporting a slew of programming ausgedehntuages.
  • FluentD: This cross-platform OSS data collection software standardizes and eases the process of unifying, collecting, and consuming data in the Kubernetes application development and zeitgemäßization process.
  • TrustyAI: This Red Hat initiative is a toolkit and containerized service providing fairness metrics, explainable AI algorithms, and other tools at the library level for use by DevOps teams deploying a Kubernetes-based app.

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