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 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