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Real-Time Analytics with Streaming AI

Build real-time AI systems for instant insights. Stream processing, event-driven architecture, and low-latency inference.

Rottawhite Team13 min readNovember 22, 2024
Real-Time AnalyticsStreamingEvent-Driven

The Need for Real-Time AI

Many applications require immediate insights and actions, from fraud detection to personalization.

Real-Time vs Batch

Batch Processing

  • Scheduled runs
  • Historical data
  • Higher latency
  • Simpler architecture

    • Real-Time Processing

  • Continuous processing
  • Live data
  • Low latency
  • Complex architecture

    • Streaming Technologies

    Message Queues

  • Apache Kafka
  • Amazon Kinesis
  • Apache Pulsar

    • Stream Processing

  • Apache Flink
  • Spark Streaming
  • Apache Beam

    • Event-Driven

  • AWS Lambda
  • Azure Functions
  • Cloud Run

    • Real-Time ML Patterns

    Feature Computation

  • Real-time features
  • Feature stores
  • Online aggregations

    • Model Serving

  • Low-latency inference
  • Model optimization
  • Caching strategies

    • Online Learning

  • Continuous updates
  • Concept drift handling

    • Architecture Patterns

    Lambda Architecture

  • Batch + streaming
  • Accuracy + speed

    • Kappa Architecture

  • Streaming only
  • Simpler design

    • Use Cases

  • Fraud detection
  • Personalization
  • Anomaly detection
  • Predictive maintenance
  • Dynamic pricing

    • Implementation Considerations

  • Latency requirements
  • Scale needs
  • Consistency guarantees
  • Fault tolerance

    • Conclusion

    Real-time AI enables immediate, intelligent responses to streaming data.

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