Detect and classify anomalies with machine learning algorithms

How it works

1. 1.Automatically load datasets from Google Cloud Storage
2. 2.Process data and apply custom classification algorithms via code execution
3. 3.Run anomaly detection and filter suspicious patterns
4. 4.Send classified and detected results to Qdrant vector database
5. 5.Route results conditionally based on detection outcomes

Use cases

• Monitor data quality and detect faults in IoT systems and sensor networks
• Classify anomalous behaviors in financial transactions and security applications

Requirements

• Google Cloud account with Cloud Storage access permissions
• Qdrant server configured and accessible over the network
• Knowledge of classification and anomaly detection algorithms

Service Value

Ready-made workflow template for automation delivery and service execution.