Designed a high-throughput and highly-available ETL pipeline to process IoT data. Pioneered Schneider's Machine Learning Platform to scale and automate analytics deployment.
- Designed a real-time ingestion pipeline for IoT data, based on the Hadoop ecosystem.
- Increased pipeline maintainability and engineer productivity by modeling the ingest pipeline as a finite state machine. Monitored critical state transition with messages collected on Azure Service Bus, giving visibility to SREs to ensure processing-time SLA compliance.
- Reduce analytics component deployment by implementing a Machine Learning framework to automate data preparation and manage ML models' lifecycle.
Leveraged knowledge in domain and data modeling, Python 3.7, Microsoft Azure (especially its messaging product Azure Service Bus), Hadoop ecosystem for big data processing (especially Apache NiFi and Kafka)
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