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--- |
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language: |
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- en |
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tags: |
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- ai |
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- observability |
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- ai-observability |
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- unsupervised-learning |
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- anomaly-detection |
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- model-drift |
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- llm-monitoring |
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- mlops |
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- aiops |
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- time-series |
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--- |
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# Model Card for Model ID |
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<# InsightFinder AI Observability Model – Unsupervised Anomaly Detection for AI and IT Systems |
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## 🧠 Overview |
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**InsightFinder AI** leverages **patented unsupervised machine learning algorithms** to solve the toughest problems in enterprise AI and IT management. Built on real-time anomaly detection, root cause analysis, and incident prediction, InsightFinder delivers AI Observability and IT Observability solutions that help enterprise-scale organizations: |
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- Automatically identify, diagnose, and remediate system issues |
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- Detect and prevent ML model drift and LLM hallucinations |
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- Ensure data quality in AI pipelines |
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- Reduce downtime across infrastructure and applications |
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This model is a core component of the InsightFinder platform, enabling **real-time, unsupervised anomaly detection** across time-series telemetry data — without requiring any labeled incidents or predefined thresholds. |
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👉 Visit [www.insightfinder.com](https://www.insightfinder.com) to learn more. |
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## 🔍 Key Capabilities |
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- **AI-native observability** across services, containers, AI pipelines, and infrastructure |
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- **Unsupervised anomaly detection** with no human labeling |
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- **Streaming inference** for real-time incident prevention |
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- **Root cause heatmaps** across logs, traces, and metrics |
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- **Detection of AI-specific issues**: model drift, hallucinations, degraded data quality |
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## 🧰 Primary Use Cases |
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- Observability for AI/ML pipelines (model/data drift, hallucinations) |
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- Monitoring large-scale cloud and hybrid infrastructure (Kubernetes, VMs, containers) |
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- IT incident prediction and proactive remediation |
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- Log and trace correlation to uncover root causes |
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- Edge system anomaly detection (IoT, on-prem) |
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## ⚙️ Model Architecture |
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- **Architecture**: Variational Autoencoder or Transformer-based time series model *(customizable)* |
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- Multivariate, asynchronous time-series support |
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- Self-learning capability with streaming updates |
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- Trained on production-grade telemetry from real-world environments |
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## 📥 Input Format |
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- Time-series telemetry from: |
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- Prometheus |
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- OpenTelemetry |
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- Fluentd / Fluent Bit |
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- AWS CloudWatch, Azure Monitor |
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- Format: JSON or CSV with `timestamp`, `metric_name`, `value`, optional metadata |
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## 📤 Output |
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- **Anomaly score** (0–1) |
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- **Anomaly classification** (binary) |
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- **Root cause probability heatmap** |
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- **Flags for drift or AI model issues** (optional) |
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## 📊 Evaluation Metrics |
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- **Precision, Recall, F1-Score** on synthetic and real production incidents |
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- **ROC-AUC** for anomaly score thresholds |
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- **Latency**: Sub-second inference (<500ms average) |
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## 📦 Training Data |
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- **Anonymized telemetry** from: |
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- Microservices and cloud infrastructure |
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- Application logs, service traces |
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- AI/ML pipeline signals |
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- No labels or annotations required |
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- Periodic retraining and adaptive learning supported |
