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How an AIOps platform can shift left–and why it should

As organizations layer more technologies into their DevOps toolchains, an observability-based AIOps platform that can shift left is a good strategy.

Since the term artificial intelligence for IT operations (AIOps) was coined by Gartner in 2016, organizations have considered it a good strategy to adopt an AIOps platform or AIOps tools. These tools can help manage and automate anomaly detection and incident response for IT operations in production environments.

But as organizations adopt CI/CD practices and layer in a growing array of cloud-native solutions and open-source technologies to their DevOps toolchains, it’s becoming clear that AIOps can unlock value along the entire digital value chain.

Data in modern cloud-native environments is a continuum—from software development through service delivery all the way to customer interactions. Everything that happens provides telemetry to help teams discover root causes, inform decisions, and automate processes.

In this increasingly integrated landscape, organizations benefit not just from an AIOps platform, but an all-in-one observability, deterministic AI, and analytics platform—a software intelligence platform—to continuously automate, analyze, predict, and remediate IT issues while navigating their digital acceleration journey.

AI applied to cloud ops

As the size and complexity of distributed cloud computing systems continue to grow, typical AIOps approaches to monitoring, diagnosing, and repairing software are not scaling. Traditional AIOps relies on correlating data in order to reduce alerts, which is slow and inaccurate and does little to identify root causes.

An intelligent AIOps platform with end-to-end observability can leverage AI-based algorithms and real-time data analytics to automate triaging, response, and remediation for common IT issues, including unexpected downtime, system latency, or determining why a Kubernetes pod was terminated.

An integrated platform that includes AIOps, observability, and analytics can consume and analyze the increasing volume of cloud data to automate and optimize these routine monitoring and management tasks. Such an observability-based AIOps platform can also provide advanced insight for IT and DevOps, while reducing mean time to resolution (MTTR) and speeding up mean time to discovery (MTTD). With end-to-end visibility into multicloud environments, an intelligent AIOps platform with advanced analytics enables faster innovation, higher quality, more efficiency, and ultimately, better business outcomes.

In addition to driving enterprise automation, there are six key capabilities an all-in-one AIOps platform approach delivers:

  1. Alert management
    • Replaces monitoring tool alert storms with accurate, reliable root-cause analysis.
    • Eliminates up to 90% of false alarms and reduces noise with deterministic AI fault tree analysis.
    • Observes, analyzes, and enables automated response in near-real time.
  2. Automation
    • Contextualizes and processes large volumes of operational data.
    • Uses this high-fidelity, context-rich collected data to create real-time topology and service flow maps.
    • Provides analysis and AI-powered insights across the application lifecycle.
    • Continuously discovers changes to environments, apps, and services.
  3. Incident prioritization and routing
    • Delivers relevant insights to the right people at the right time, providing precise answers with root-cause determination, prioritized by business impact.
  4. Event causation
    • Uses causation-based AI to point directly to the root cause and impact of a failed test run, an application slowdown, or system outage, or to drive decisions about whether to release a piece of software.
  5. Predictive analytics
    • Monitors the entire technology stack end-to-end to predict and prevent future disruptions before they occur.
  6. Auto-remediation
    • Automates anomaly detection, problem notification, and self-remediation with full-stack monitoring and integration with workflow automation platforms, such as ServiceNow and Jenkins.

Why AIOps needs to “shift-left”

With the volume of data increasing, and the demand for services rocketing upward, the need for AIOps is no longer limited to IT operations. As DevOps and SRE practices mature, pre-production workflows need AIOps capabilities just as acutely.

A typical continuous integration/continuous delivery (CI/CD) pipeline follows the following sequence:

  1. Source — creating source code
  2. Build — compiling the application
  3. Test — testing code for functionality
  4. Release — pushing code to the repository
  5. Deploy — moving code to production

The term “shift-left” refers to the practice of performing a task at an earlier stage of development before it goes to production, such as automated testing at the source phase instead of when code is ready to be released.

Shift-left applied to AIOps integrates AI into the full DevOps lifecycle, including data ingestion, building code, and testing for enhanced software quality and deeper root-cause analysis before code is deployed to production. A software intelligence platform that includes end-to-end observability in its approach to AIOps delivers continuous alert and incident management, automatically observes and identifies anomalies in CI/CD pipelines, and prevents issues from reaching the production stage, resulting in more efficient builds and quicker, higher-quality releases of new versions of software.

Shifting AIOps left means development teams can easily leverage production service-level objectives (SLOs) as criteria for building quality gates into acceptance testing earlier in the development cycle. It also means teams can initiate auto-remediation for CI/CD workflows by integrating with software configuration and deployment management technologies, such as Chef, Puppet, and Ansible.

Faster time-to-value with an intelligent AIOps platform

An AIOps platform based on continuous discovery and end-to-end observability can detect anomalies before they affect the CI/CD pipeline or impact customer experience and SLOs. It can automate validation processes by using SLO-based quality gates with events, tags, and APIs integrating seamlessly with existing CI/CD workflows — for faster automated deployments and time-to-value.

AI-enhanced alerting and escalation using advanced algorithms based on deterministic fault-tree analysis can automatically route incidents to the appropriate team, empowering them with metadata and context that results in accurate, reliable, and precise root-cause analysis. If automated processes are unable to address a slowdown or outage, DevOps is then given a clear path to remediation, eliminating time spent on “problem triage,” which drives faster innovation and better quality.

A shift-left AIOPs platform approach mitigates the cost of IT downtime

Every CIO and CFO knows IT downtime is costly — potentially adding up to thousands of dollars per minute or more depending on the organization’s size and reach of services. But what may be as significant is the impact downtime and performance issues can have on already pushed-to-the-limit DevOps and IT teams. They are under increasing pressure to maintain system reliability and prevent outages of highly complex, distributed multicloud operations. The constant context switching required to hop from issue to issue also derails focus on mission-critical concerns and distracts teams from their core functions.

By shifting AIOps left using production-based performance criteria as a quality gate earlier in the development cycle, teams can release more resilient software. If an issue arises anywhere in the DevOps workflow, an integrated AIOps platform with all-in-one observability, deterministic AI, and analytics can automatically remediate issues and optimize performance based on system health and user demands — preventing or minimizing the duration of outages and reducing costs. It also combats IT teams’ top stressors by reducing the need for manual intervention in IT operations and DevOps workflows, helping to prevent burnout and costly employee churn.

Shift AIOps left with an integrated software intelligence platform

Teams undergoing digital transformation are discovering that shifting AIOps left into DevOps and SRE workflows can accelerate and increase the effectiveness of their DevOps and SRE initiatives.

To help developers pinpoint problems and automate more processes during the development, test, and delivery phases of DevOps, Dynatrace seamlessly integrates AIOps into the CI/CD pipeline, bringing fault-tree analysis to pre-production workflows. Shifting AIOps left enables developers to discover and auto-remediate issues in pre-production so they can optimize processes and deliver higher quality code to production.

Using the Cloud Automation control plane—powered by Keptn, an open-source technology for cloud-native application life-cycle orchestration—Dynatrace provides release analysis, version awareness, and SLO-based quality gates so teams can automate releases at all stages of the DevOps pipeline. By integrating with DevOps tools like Chef, Puppet and Ansible, Dynatrace can execute closed-loop remediation workflows or orchestrate ITSM tools to trigger incident management workflows.

To learn more about how Dynatrace approaches AIOps, see the eBook: AIOps Done Right.

To see Dynatrace AIOps in action, join us for the on-demand Performance Clinic, No metric left alone: Feeding all your observability data into Dynatrace AIOps.

Dynatrace was also named a leader in AIOps in the Forrester Wave. Read the report here.