Runtime Inteligence
A runtime intelligence is a critical component of modern data-driven organizations. By combining real-time data processing, feature engineering, and machine learning capabilities, it empowers businesses to make faster, more informed decisions and gain a competitive edge.
Why?
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Reuse Derived Features: Leverage runtime insights (e.g., model confidence, recent predictions) as features in future models or business logic.
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Enhance Observability: Continuously monitor model behavior, detect drift, and identify anomalies in production.
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Streamline Feature Pipelines: Highlight valuable features and eliminate redundant or low-impact ones based on real-time usage.
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Support Time-bound Features: Provide deterministic, aggregated features over sliding or fixed time windows for accurate, time-aware predictions.
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Enable Feedback Loops: Store user interactions and outcomes alongside predictions to support online learning and personalization.
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Power Real-time Decisioning: Fuse live inference with historical context to make fast, informed decisions within latency constraints.
1
Inference Telemetry Collector
Captures real-time metadata during model inference — including inputs, outputs, model versions, latency, confidence scores, and explainability metrics. It streams this data into a real-time processing pipeline like Kafka or Kinesis.
2
Streaming & Batch Processors
Streaming Processor
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Processes telemetry in real-time to generate derived metrics like user/session-level aggregates, sliding window stats (e.g., avg confidence), and filtered event triggers.
Batch Processor
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Runs scheduled jobs (e.g., hourly or daily) to compute historical trends, enrich features, and summarize model behavior for monitoring or retraining.
3
Runtime Intelligence Store (RI Store)
A centralized storage layer for deterministic, derived insights from model inference — such as aggregated results, feature stats, and user behavior. Combines online (e.g., Redis) for fast access and offline (e.g., S3, BigQuery) for batch analytics.
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Feature/Insight Serving Layer
Serves runtime intelligence as features or signals for real-time inference, online learning, and monitoring. Supports low-latency API lookups and batch queries for experimentation and reporting.
5
Metadata & Governance Layer
Maintains traceability of features and insights by tracking model versions, data lineage, update timestamps, and guarantees around determinism and freshness.