In the LM Envision platform, anomaly detection for metrics is referred to by the feature name “Dynamic Threshold” rather than the more generic machine learning term “anomaly detection.” Dynamic thresholds allow users to identify and set custom alert thresholds based on observed data points. Metric thresholds in rules-based systems are effective when the desired outcome is clear. However, static thresholds may not anticipate emerging issues.

Rules are excellent when you know precisely what you want to match, typically based on experience. Yet rules only let you observe what you have learned to look for. This is where artificial intelligence (AI) and machine learning (ML) contribute significantly to observability – detecting errors and early warning signs that were previously unobservable. LM Envision supports metric and log anomaly detection. This blog discusses how LM Envision Log Anomalies uncovers previously unknown anomalies.

IT operations are a constant juggling act. Too many tools, too much data, and not enough time to proactively avoid outages. But what if AI could not only streamline your tools but help you make sense of the chaos? That’s the power of AIOps.