Building Scalable AI/ML Platforms for Industrial IoT
summary
Platform description gives concrete claims and components: 1.5–2.3 TB/day of sensor data, hybrid AWS and Azure architecture with snowflake analytics, cubeflow pipeline, Kofka and Pulsar for ingestion, edge preprocessing and mutual auth for device updates. Reported outcomes: 89.7% prediction accuracy, 385% ROI, 50% downtime reduction and 8–12 days early warning; a 37‑site case yields ~42% downtime cut, $4.3M annual savings and 93% model deployment success, so the platform demonstrably works.
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chapters (14)
- 0:00Introduction and Speaker Background
- 0:46Agenda Overview
- 1:26Industrial IoT Landscape
- 2:11From Reactive to Predictive Maintenance
- 3:21Business Use Case for AI-Powered Maintenance
- 4:45Core Architectural Patterns for IoT Success
- 6:01Cloud Native Platform Components
- 7:08Edge Computing Integrations
- 8:18ML Pipeline Orchestration
- 9:27Developer Experience and Enablement
- 10:15Case Studies and Real-World Implementations
- 11:01Implementation Roadmap
- 11:54Key Takeaways for Platform Engineers
- 12:54Conclusion and Q&A
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