Create README.md

# 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.

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## 🔍 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

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## 🧰 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)

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## ⚙️ 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

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## 📥 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

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## 📤 Output

- **Anomaly score** (0–1)
- **Anomaly classification** (binary)
- **Root cause probability heatmap**
- **Flags for drift or AI model issues** (optional)

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## 📊 Evaluation Metrics

- **Precision, Recall, F1-Score** on synthetic and real production incidents
- **ROC-AUC** for anomaly score thresholds
- **Latency**: Sub-second inference (<500ms average)

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## 📦 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

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## ⚠️ Limitations

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

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## 🧪 Quick Usage Example

```python
from transformers import AutoModel

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

# Add your inference logic here

README.md

@@ -0,0 +1,15 @@
+---
+language:
+- en
+tags:
+- ai
+- observability
+- ai-observability
+- unsupervised-learning
+- anomaly-detection
+- model-drift
+- llm-monitoring
+- mlops
+- aiops
+- time-series
+---