Netreo announced the release of AIOps: Autopilot, featuring data models that combine artificial intelligence (AI) and machine learning (ML) technology with 20 years of network management system (NMS) configuration and monitoring data.
As a result, the new porduct brings the intelligence to automatically discover, configure and tune the entire monitoring environment. This allows threshold baselines, event correlation rules, dependency mapping, and many other configurations to evolve and improve the longer Netreo is deployed.
AIOps: Autopilot extends the capabilities of the Netreo full-stack IT management solution, which enables enterprises to easily measure the state, impact and operation of IT resources, so they can focus on their core business. The add-on works with both the on-premises and native-cloud versions of Netreo’s solution, including Netreo Cloud, also announced today. (See Netreo Cloud press release dated February 26, 2020, for more information.)
AIOps: Autopilot runs in the background of a Netreo deployment and constantly scans the configuration to make sure it is always tuned properly. When issues or potential improvements are found, AIOps: Autopilot will either automatically fix the problem or provide engineers suggested remediations using AI and ML algorithms applied against previously gathered historical data. Significantly, AIOps: Autopilot automatically learns from every successive execution and gets smarter, so that it can both reduce unnecessary alerts and preemptively correct more issues over time.
“The key to transforming IT into a strategic weapon for an organization is to employ tools that can observe your whole technology stack, analyze the data found, and act on those findings,” said Netreo President Andrew Anderson. “AIOps: Autopilot was designed and developed with that idea in mind. It takes care of learning and automatically configuring the monitoring environment, so there are never any blind spots. As a result, engineering teams can take complete visibility for granted and spend their time engineering, not tuning their tools.”
AIOps solutions are only as good as the data and context fed into them for model creation. AIOps: Autopilot integrates two decades of pragmatic use cases that have made a difference in real-world deployments when deciding how to tune the Netreo application. Using AIOps: Autopilot, network and system administrators can spend less time tuning their NMS platforms and spend more time supporting end-users, engineering, and meeting committed service-level agreements (SLAs).
“It’s been my experience that enterprise systems and network engineers budget 50% of their time for strategic initiatives, architecture, and design. In reality, the split is more like 80/20 with the majority of time getting spent chasing down problems,“ says Trace3 Engagement Architect Ron Cameron. “AIOps: Autopilot from Netreo is an important step in helping to ease this burden by right-sizing that ratio, so technicians spend more time engineering and less time fire-fighting.”
AIOps: Autopilot builds on Netreo’s solid backbone of other AI- and ML-based technologies, such as anomalistic behavior detection, automatic dependency mapping, and event correlations. Although it gets better at its job the longer it runs in an environment, AIOps: Autopilot also comes with an array of extensions out-of-the-box to give operations teams a head start. These extensions provide the ability to:
- Automatically baseline thresholds against historical values and exceptions to minimize false-positives and alert noise.
- Model all metrics against best-practice key performance indicators (KPIs) to ensure there are no blind spots.
- Learn system and environment changes, and change the monitoring infrastructure to automatically adapt.
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