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Centreon AIOps Extension: How AI in Monitoring Can Skyrocket IT Operations Team Productivity

Blog Centreon AIOps Extension: How AI in Monitoring Can Skyrocket IT Operations Team Productivity

Artificial intelligence (AI) has been making headlines for several months, often featuring the instantly famous ChatGPT. But away from the projectors, AI is quietly taking its place in our daily lives, and is now integrated in your Centreon monitoring solution.

With the Centreon AIOps Extension, explore how AI and its predictive analysis capabilities can enhance your productivity by proactively detecting anomalies and improving responsiveness for optimized performance.

Leveraging AI for Proactivity and Predictability in Monitoring

Artificial Intelligence (AI) and Machine Learning (ML) technologies are enhancing the efficiency and resilience of IT systems, playing a crucial role in automation. They provide a valuable addition to monitoring tools, complementing scripts and discovery functions. These technologies are poised to become genuine assistants for IT teams, allowing them to utilize AI for anticipating and resolving incidents.

“The more artificial intelligence, the better for us! AI will enable us to carry out pre-diagnostics. Some of the analysis can be done by machines rather than by humans. Another essential point is the ability of the monitoring console to learn from existing incidents through Machine Learning and suggest solutions.”
Eric D. – Manager, Communication Infrastructure and Projects at a hospital group (France).

The ability to automatically detect anomalies using AI and ML liberates you from the constraints of traditional monitoring thresholds, even opening the door to auto-suggestion features that simplify issue resolution for ITOPs teams. The future is now!

Thanks to artificial intelligence and machine learning technologies, monitoring will be able to trigger corrective, and even preventive, actions without human intervention.” 
Nirina R., Head of the Operations and Production at the French Department of Justice.

Centreon AIOps Extension: AI Applied to the Centreon Platform

The AIOps (Artificial Intelligence for IT Operations) Extension is a SaaS offering available with Centreon 23.04 and upward. It integrates  AI and ML algorithms that enable the detection of anomalies that may otherwise go unnoticed, enhancing the productivity of ITOps teams.

The extension is available for both the SaaS (Centreon Cloud) and on-premise offers..

What are AIOPs exactly? AIOps refers to the use of big data to automate the diagnosis and resolution of incidents or issues within an organization’s IT systems, through machine learning and artificial intelligence algorithms. AIOps  rely on performance or log data from an organization’s computer systems, services, and applications.

Key Benefits of the Centreon AIOps Extension:

  • IT monitoring generates vast amounts of data that only artificial intelligence (AI) and machine learning (ML) can fully analyze. The AIOps extension deploys AI/ML algorithms to enhance the efficiency of IT operations teams.
  • Anomaly detection leverages automatic and dynamic thresholds using AI/ML filtering techniques. Thanks to anomaly detection, IT operations teams no longer miss alerts due to poorly configured thresholds.
  • Predictive capability enables the prediction and alerting of when a system reaches its maximum capacity, even when the consumption model is complex.
  • Outlier detection enables the observation of clusters with load balancing rules and issues alerts when one of the devices behaves differently from the others.

A Closer Look at Anomaly Detection and Predictive Capability

Anomaly Detection has been accessible to all users since Centreon 23.04. With Anomaly Detection, Centreon’s machine learning (ML) technologies continuously learn from a metric’s typical behavior to predict its expected value and alert if the monitored value deviates significantly from the expected value. Here’s what the Centreon AIOps Extension enables:

  • Automatic recommendations of metrics that could benefit from anomaly detection.
  • Fine-tuning the acceptable deviation margin so alerts will only be triggered beyond this margin, and the option to exclude irrelevant data for a more accurate model.  No more fixed alerts and critical thresholds!
  • Fully integrated anomaly detection into all features, such as in business activity monitoring, for example.
  • Anomaly detection as a SaaS feature but  available to both Centreon on-premises and Centreon Cloud users. A dedicated stream connector securely sends data from your Centreon platform to the AIOps Extension service in the cloud.

Leveraging this extension, ITOps will  no longer miss alerts or deal with alerts that are triggered by misconfigured thresholds.

Predictive Capacity: Centreon’s ML technology can learn from a metric’s behavior to build an accurate forecasting model. Capacity forecasts are much more precise and accurate than the simple linear regression typically found in capacity planning tools. Users can set thresholds and choose when to be alerted, giving them time to act before the threshold is reached.

This functionality facilitates the daily work of monitoring teams, allowing them to focus on problem resolution rather than detection.

Would you like  to test this extension? Easy, join the group of beta testers: “Centreon AIOps Extension Users” on The Watch, Centreon’s community!

Benefits of AI in Monitoring

Here’s what incorporating AI into IT monitoring can provide in your daily operations:

  • Early anomaly detection: AI enables real-time analysis of large amounts of data to detect anomalies well before they cause major malfunctions, reducing downtime
    and disruption.

“Centreon Anomaly Detection is another handy feature that we use to discover some issues. I estimate that using Anomaly Detection has cut our resolution time in half.”
Monitoring Manager at a transportation company. Read the PeerSpot Review.

  • Improved responsiveness: Thanks to AI, problems can be detected and corrected in real time, reducing unplanned downtime and improving the monitoring team’s responsiveness to incidents.
  • Streamlined Alert Automation: AI makes setting up alerts more straightforward and adaptable with adjustable thresholds.

“We use the solution’s Anomaly Detection feature. It helps our teams to predict potential issues. It also automates the alerting process.”
Mamadou D. – IT Analyst at a public emergency medical service (Canada). Read the PeerSpot Review.

  • Predictive analysis: AI can use machine learning models to anticipate potential problems by analyzing past trends and predicting future patterns, helping to take preventive action.
  • Performance optimization: By analyzing performance data in real time, AI can recommend adjustments to optimize resource utilization and improve overall system performance.
  • Cost reduction: By better detecting problems, preventing downtime and automating tasks, the use of AI can reduce the operational costs associated
    with IT monitoring.

Going Further :


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