Update README.md

README.md

@@ -12,4 +12,90 @@ tags:
 - mlops
 - aiops
 - time-series
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+---
+# Model Card for Model ID
+
+<# InsightFinder AI Observability Model – Unsupervised Anomaly Detection for AI and IT Systems
+
+![InsightFinder](https://www.insightfinder.com/wp-content/uploads/2022/04/InsightFinder_logo.png)
+
+## 🧠 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