AI & Machine Learning · Reliability
See where your cluster is headed before users do: Overview ties a risk score and incident probability to compact recommendations, trend charts, and forecast rows with horizons and confidence. Fleet Risk rolls assessments across clusters by provider, region, and environment. Audit streams policies, planned actions, reinforcement-learning feedback, and alerts in one filterable feed. Under the hood, signal pipelines ingest Kubernetes events, GPU and hardware posture, anomaly features, topology RCA, and predictive HPA signals—so blast radius and drivers stay explainable.
Every frame below is a real Cloud Admin screen—paired with plain-language context so you know what you are looking at and why it matters operationally.
Your starting point for predictive maintenance: KPI cards and charts summarize fleet health, including predictive maintenance risk score and recommendations. Spot drift early, then drill into the tab that explains the root cause.
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Actionable signals instead of raw logs—fleet risk breakdown by provider and region. Each item ties back to predictive maintenance so owners know what to fix now versus what can wait.
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Actionable signals instead of raw logs—predictive maintenance audit alerts. Each item ties back to predictive maintenance so owners know what to fix now versus what can wait.
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When risk, RCA, and audit share one narrative, incident reviews start with evidence instead of anecdotes.
Gauge and classifier probability sit next to recommendation copy tied to rationale fields.
Rollups show where to spend the next hour—not every cluster equally.
Policy and RL events land beside alerts so automation stays reviewable.
Run the risk engine from Cloud Admin alongside AI workloads, metrics, and fleet operations.
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