ML KPIs and Automated Insights

Automated Detection of Problems, Deviations, and Abnormal Behaviours

We have various techniques for baselining, scoring, and detecting anomalies. The detection can be proactive or predictive, depending on your needs. We use different machine learning techniques—supervised or unsupervised—based on the type of data and how mature the environment is. Below, we’ll discuss specific use cases, techniques, and how to deploy them.

Transforming the insights gathered through Observability, we distill them into journey metrics and CXO Dashboard KPIs. This process involves deriving indices that communicate the satisfaction of customers, the system’s performance, and the adequacy of system provisioning.

• User Experience Index
• Operational Predictive Index
• Current Health Index
  • Server Health Index
  • Network Health Index
• ML Automated Insights

By leveraging the outlined ML techniques, organizations can adeptly tackle complex issues related to business journeys, infrastructure components, and networks. This approach optimizes operations and ensures an outstanding user experience. Proactively identifying and resolving problems contributes to improved system reliability, reduced downtime, and heightened end-user satisfaction.

To continually enhance the accuracy and effectiveness of these ML models, timely feedback is crucial. Gathering feedback on the model’s predictions and outcomes allows organizations to fine-tune ML algorithms, retrain models, and improve performance over time. This feedback loop empowers models to learn from real-world scenarios, adapt to evolving conditions, and refine their predictive capabilities.

For ML techniques to yield better results, providing the necessary data that meets the following criteria is essential:

Further Reading

1. User Experience Index
2. Operational Predictive Index
3. Current Health Index
4. ML Automated Insights