Ensuring the continuity of critical industrial systems at scale
Machine Learning-based AIOps solution to anticipate application failures, reduce downtime, and secure industrial workflows.
Contexte
Airbus manages an ecosystem of 56 interconnected critical applications. Unforeseeable incidents and latency issues hinder operational efficiency, directly impacting industrial production. Proactively identifying bottlenecks has become a vital imperative to ensure service continuity in the face of growing demand.
Solution
We have deployed an advanced predictive modeling solution based on neural networks. By correlating application health logs with business workflows, the tool anticipates incidents and system overloads. This AIOps approach optimizes infrastructure performance without destabilizing the existing complex architecture.
Bénéfices
- Reduced downtime through early detection of critical incidents.
- Improved data flow between software components, essential for production.
- Informed decision-making for IT leadership, ensuring stability during traffic spikes
"The complexity lay in the interdependence of dozens of heterogeneous applications. We used multilayer perceptrons to capture non-linear correlations between data streams. This approach transformed reactive alerts into reliable forecasts, giving IT teams a head start on system failures."
Make your industrial IT ecosystem more reliable
Optimize your application availability with predictive AI
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