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---
language:
- en
tags:
- ai
- observability
- ai-observability
- unsupervised-learning
- anomaly-detection
- model-drift
- llm-monitoring
- mlops
- aiops
- time-series
---
# Model Card for Model ID
<# InsightFinder AI Observability Model – Unsupervised Anomaly Detection for AI and IT Systems
## 🧠 Overview
**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:
- Automatically identify, diagnose, and remediate system issues
- Detect and prevent ML model drift and LLM hallucinations
- Ensure data quality in AI pipelines
- Reduce downtime across infrastructure and applications
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.
👉 Visit [www.insightfinder.com](https://www.insightfinder.com) to learn more.
---
## 🔍 Key Capabilities
- **AI-native observability** across services, containers, AI pipelines, and infrastructure
- **Unsupervised anomaly detection** with no human labeling
- **Streaming inference** for real-time incident prevention
- **Root cause heatmaps** across logs, traces, and metrics
- **Detection of AI-specific issues**: model drift, hallucinations, degraded data quality
---
## 🧰 Primary Use Cases
- Observability for AI/ML pipelines (model/data drift, hallucinations)
- Monitoring large-scale cloud and hybrid infrastructure (Kubernetes, VMs, containers)
- IT incident prediction and proactive remediation
- Log and trace correlation to uncover root causes
- Edge system anomaly detection (IoT, on-prem)
---
## ⚙️ Model Architecture
- **Architecture**: Variational Autoencoder or Transformer-based time series model *(customizable)*
- Multivariate, asynchronous time-series support
- Self-learning capability with streaming updates
- Trained on production-grade telemetry from real-world environments
---
## 📥 Input Format
- Time-series telemetry from:
- Prometheus
- OpenTelemetry
- Fluentd / Fluent Bit
- AWS CloudWatch, Azure Monitor
- Format: JSON or CSV with `timestamp`, `metric_name`, `value`, optional metadata
---
## 📤 Output
- **Anomaly score** (0–1)
- **Anomaly classification** (binary)
- **Root cause probability heatmap**
- **Flags for drift or AI model issues** (optional)
---
## 📊 Evaluation Metrics
- **Precision, Recall, F1-Score** on synthetic and real production incidents
- **ROC-AUC** for anomaly score thresholds
- **Latency**: Sub-second inference (<500ms average)
---
## 📦 Training Data
- **Anonymized telemetry** from:
- Microservices and cloud infrastructure
- Application logs, service traces
- AI/ML pipeline signals
- No labels or annotations required
- Periodic retraining and adaptive learning supported |