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Real-Time Streaming AI Pattern

A data processing pattern that applies AI models to continuous data streams in real-time, enabling immediate responses and continuous learning from streaming data sources. This pattern supports low-latency AI applications that need to process data as it arrives.

When to Use

Use this pattern for applications requiring immediate AI responses to streaming data, real-time decision making, or continuous model adaptation. Essential for fraud detection, real-time personalization, IoT analytics, and live content moderation systems.

Architecture Drawing

The architecture depicts streaming data flowing through real-time processing engines with AI models that can make immediate decisions and optionally trigger actions or updates.

Components: - Data Sources (100, 50) - Stream Ingestion (200, 50) - Event Router (300, 50) - Stream Processor A (200, 150) - Stream Processor B (400, 150) - AI Model Service (300, 200) - Decision Engine (450, 200) - Action Triggers (550, 200) - State Store (200, 250) - Monitoring Dashboard (450, 250)

Edges: - Data Sources → Stream Ingestion: "Raw Data Stream" - Stream Ingestion → Event Router: "Processed Events" - Event Router → Stream Processor A: "Event Stream A" - Event Router → Stream Processor B: "Event Stream B" - Stream Processor A → AI Model Service: "Feature Extraction" - Stream Processor B → AI Model Service: "Feature Extraction" - AI Model Service → Decision Engine: "AI Predictions" - Decision Engine → Action Triggers: "Automated Actions" - Stream Processor A → State Store: "State Updates" - AI Model Service → Monitoring Dashboard: "Performance Metrics"

Event-Driven Architecture Pattern, Lambda Architecture Pattern, Edge Computing Pattern

References

  1. Stream Processing with Apache Kafka and Machine Learning - 2020 - Confluent - Practical guide for implementing real-time ML with streaming platforms
  2. Real-Time Machine Learning Inference at Scale - 2017 - Uber Engineering - Case study of large-scale real-time ML inference architecture
  3. Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing - 2018 - O'Reilly - Comprehensive guide to streaming data processing architectures and patterns