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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 AI delivers AI Observability and IT Observability solutions that help enterprise-scale organizations to automatically identify, diagnose, and remediate issues including model drift, LLM hallucinations, model data quality, and application and infrastructure failures. Visit www.insightfinder.com to learn more.

InsightFinder AI Observability Model – Unsupervised Anomaly Detection for AI and IT Systems

InsightFinder

🧠 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

⚠️ Limitations

  • Best performance when trained on baseline system state
  • Fine-tuning may be needed for volatile edge workloads
  • High-dimensional data can affect interpretability

🧪 Quick Usage Example

from transformers import AutoModel

# Load InsightFinder’s AI Observability Model
model = AutoModel.from_pretrained("insightfinder/ai-observability-unsupervised-anomaly-detector")

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