GCP Real-Time IoT Analytics Platform with ML Inference
About This Architecture
End-to-end GCP IoT analytics platform ingesting 10GB/day from 50,000+ devices through Cloud IoT Core and Pub/Sub into Dataflow streaming pipelines. Real-time telemetry flows through validation, enrichment, and 5-minute windowing before landing in BigQuery, while Cloud Composer orchestrates hourly Spark batch jobs on Dataproc for feature engineering. Vertex AI handles the complete ML lifecycle from training on BigQuery features to serving predictions via auto-scaling endpoints with minimum 2 replicas. Fork this architecture on Diagrams.so to customize the streaming windows, adjust Dataproc cluster sizing, or swap in your own ML model deployment strategy.
People also ask
How do I build a real-time IoT analytics platform with ML inference on GCP?
This GCP architecture ingests IoT data via Cloud IoT Core and Pub/Sub, processes streams through Dataflow into BigQuery, runs Spark batch jobs on Dataproc for feature engineering, and serves ML predictions through Vertex AI endpoints.
- Domain:
- Ml Pipeline
- Audience:
- GCP data engineers and ML engineers building production IoT analytics platforms
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