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Data & Analytics
Real-Time Analytics with Streaming AI
Build real-time AI systems for instant insights. Stream processing, event-driven architecture, and low-latency inference.
Seena Singh 13 min readNovember 22, 2024
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.
Real-Time AnalyticsStreamingEvent-Driven
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