AI for IT Operations


AI for IT Operations

Transformation of ITOM and ITSM is underway with the arrival of what Gartner calls “Artificial Intelligence for IT Operations” or AIOPs. This is basically the merging of ITSM, ITOM and IT Automation, providing multi-layered technology platforms to automate and enhance IT operations. The AIOps approach isn’t a replacement for ITIL, but it will undoubtedly complement it as organizations face growing challenges raised by the speed and complexity of digital transformation. AIOps will enable to predict service outrages and automate root cause analysis.

Bring the power of machine learning and analytics to IT Operations. Innovate faster, reduce operational cost and transform IT operations (ITOps) across a changing landscape with an AIOps solution that delivers visibility into performance data and dependencies across environments. Embrace AI and automation to help ITOps managers and Site Reliability Engineers (SREs) address incident management and remediation.
Our AIOps Solutions helps you to Diagnose problems faster, Build and Manage securely and Automate with confidence.

Techeva AIOps solutions deploy machine learning and advanced analytics as part of a holistic monitoring, event management, capacity and automation solution to deliver AIOps use cases that help IT Ops run at the speed that digital business demands.

Our AIOps tools apply machine learning and advanced analytics to identify patterns in monitoring, capacity, service desk, and automation data across hybrid on-premises and multi-cloud environments. Adopting AIOps empowers IT operations and observability teams to:

  • Utilize AIOps, machine learning, and anomaly detection to improve performance and availability, on-prem and in the cloud
  • Reduce event noise and prioritize business-critical issues
  • Support the speed of application releases and DevOps processes
  • Proactively identify problems and quickly drill into root cause to reduce MTTR (Mean Time to Recovery)
  • Model and predict workload capacity requirements to optimize resource usage and cost.

Implementing Techeva AIOps strategy goes beyond getting better analytics for existing data. Building the basis for a machine learning system that will yield continuous insights requires:

  • Open data access including multiple, consumable sources of historical and streaming IT data
  • Machine learning and algorithms that learn behavioral patterns of data and yield automated insights
  • Automation to act on analytical insights and engage with the ITSM Service Desk.