Observability for AI Monitoring AI Stack Health, Performance, and Security
Today, AI is rewriting the rules for observability. As agentic AI emerges, it is even coding software — making it critical for teams to have the visibility required to ensure applications perform as expected.
Metrics, events, logs, and traces (MELT) from AI environments behave differently than in traditional and even modern application environments. GPU utilisation, model latency, and data pipeline throughput matter as much as CPU or uptime, and they rarely move in predictable patterns. In isolation, these signals are noise. Pulled into a single view, they reveal the full picture of reliability, accuracy, quality, and security issues before they disrupt performance and undermine the user experience.
Metrics, events, logs, and traces (MELT) from AI environments behave differently than in traditional and even modern application environments. GPU utilisation, model latency, and data pipeline throughput matter as much as CPU or uptime, and they rarely move in predictable patterns. In isolation, these signals are noise. Pulled into a single view, they reveal the full picture of reliability, accuracy, quality, and security issues before they disrupt performance and undermine the user experience.
Anbieter: Splunk services Singapore Pte Ltd | Größe: 10,7 MB | Sprache: Englisch