Use Machine Learning to Improve Ops

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Use Machine Learning to Improve Ops

Digital operations management empowers IT Ops and DevOps to make intelligent real-time decisions with automated response and business-wide orchestration.

· AI Zone ·
Free Resource

PagerDuty is now using machine learning (ML) and advanced response automation that enable businesses to orchestrate the right business-wide response to any situation. By eliminating inefficiencies across the entire digital operations lifecycle and applying best practices to any operational issue, PagerDuty helps businesses and teams focus on innovation, enhance the brand, and protect revenue to deliver exceptional customer experience (CX).  

According to IDC, by 2020, 50 percent of the Global 2000 will see the majority of their business depend on their ability to create digitally enhanced products, services, and experiences. Yet the majority of IT operations practitioners are still manually dealing with the challenges of an increasingly complex software stack. This creates a critical gap in the resolution of incidents and customer expectations for the seamless delivery of digital services. PagerDuty’s latest capabilities mitigate these challenges through ML and automation for significant improvement across the entire digital operations management lifecycle.

Today’s dynamic digital business climate has exponentially increased both opportunity for growth and downside risks to mitigate. The latest Digital Operations Management capabilities announced today - machine learning and automation - tackle the real-time, all-the-time demands of consumers and business, translating complex events and signals into actionable insights, and orchestrating teams across businesses in service or revenue and productivity," said Jennifer Tejada, CEO of PagerDuty. “Leveraging  our foundation in DevOps, we now empower our customers with a platform that intelligently responds to events and enables the delivery of seamless digital experiences so IT and business decision makers can confidently execute on their financial, operational, and strategic imperatives.

With the new release, customers can now take advantage of the following capabilities for intelligent real-time decisions, automated precision response and business-wide orchestration:

  • Alert grouping: Rules-based automation and ML automatically group related issues together, providing critical context required to drive down resolution times while reducing responder noise.

  • Similar incidents: Machine data is critical, but to get the entire picture, responders also need human context such as who has dealt with a similar issue and what steps were taken to solve it. Now, responders can see previous related issues and surface information around incident severity, impact, remediation steps, and much more.

  • Response automation: With the new Response Plays capability, teams can design and execute an automated team response pattern, recruiting the exact right responders and stakeholders automatically or with a single tap.

  • Dynamic notifications and event routing: Dynamically select notification and assignment behavior, and automatically route events to different teams, based on event payloads.

  • Redesigned, live user experience: The enhanced live incident details page provides improved information discoverability and ease of use.

Digital disruptions require a whole-business response. PagerDuty’s new capabilities dedicate valuable resources to rapid, frictionless service restoration rather than manual work, implement business-wide best practices, and enable instant communication across critical teams and stakeholders, helping organizations save millions of dollars in downtime incidents and service degradation.

ai, devops, machine learning

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