Qdrant Anomaly Detection Setup: Cluster Centers & Thresholds [2/3]
Sets up cluster centers and threshold scores in Qdrant vector DB for anomaly detection on image datasets. Part of AI agent big data analysis series for production use.
This n8n workflow is the second in a series from the 'Build production-ready AI Agents with Qdrant and n8n' webinar, focusing on preparing a vector database for anomaly detection. It computes and stores cluster (class) centers and threshold scores using previously uploaded image datasets (e.g., agricultural crops and landuse scenes from Kaggle). By analyzing embeddings in Qdrant, it establishes reference points for identifying outliers, enabling robust anomaly detection in subsequent workflows.
- Platform
- n8n
- Category
- Education
- Price
- $19.99
- Creator
- Mateusz Eriksen
- qdrant
- vector-database
- anomaly-detection
- ai-agents
- image-analysis
- big-data
- automation
- n8n
- kaggle
- embeddings
How to import this workflow into n8n
- 1Purchase or download the workflow to get the n8n workflow JSON file.
- 2In your n8n instance, open Workflows and choose "Import from File" (or paste the JSON with Ctrl+V on the canvas).
- 3Open each node marked with a credential warning and connect your own accounts and API keys.
- 4Run the workflow once manually to verify the data flow, then toggle it to Active.
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