How AI/ML providing value in critical functions
Today and Tommorrow
Alerting:
- Traditional Approach: Manual threshold setting and reactive responses.
- AI/ML Impact: predictive maintenance alerting based on historical data patterns and real-time analysis, reducing false positives and ensuring timely responses to critical issues.
Anomalies Detection:
- Traditional Approach: Manual identification and predefined rule-based systems.
- AI/ML Impact: Continuous monitoring using machine learning models that adapt and identify unusual patterns that deviate from the norm, improving detection accuracy.
Root Cause Analysis:
- Traditional Approach: Time-consuming manual investigation and correlation.
- AI/ML Impact: Automated artificial intelligence leveraging AI to quickly correlate events and pinpoint the source of issues, speeding up resolution times.
Automation:
- Traditional Approach: Limited scope automation and manual interventions.
- AI/ML Impact: End-to-end automation using AI-driven decision-making processes, enabling proactive management and self-healing networks.
Zero Touch Operations:
- Traditional Approach: Manual configurations and updates.
- AI/ML Impact: Fully automated workflows that require minimal human intervention, leveraging AI to manage and orchestrate network operations seamlessly.
Ticketing:
- Traditional Approach: Manual ticket creation and resolution tracking.
- AI/ML Impact: Intelligent ticketing systems that automatically create, categorize, and assign tickets based on incident analysis and historical data, improving efficiency and accuracy in issue resolution..