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2024
Production ML systems
3+ years building high-throughput data pipelines and production ML infrastructure.
The story
How it was achieved
Three-plus years of building high-throughput data pipelines and production ML infrastructure — the layer between raw data and product decisions.
Systems I operate in production:
- Streaming pipelines (Kafka, Flink) processing millions of events daily
- Batch orchestration (Airflow, dbt) keeping warehouse models fresh and tested
- ML serving infrastructure: feature stores, model registries, and low-latency inference endpoints
- Monitoring and alerting (Prometheus, Grafana) with SLOs that actually get paged
Outcome
Measurable results
Results:
- Sub-minute data freshness for product analytics
- Warehouse models covered by automated tests
- Inference endpoints with p99 latency under 100ms
- Reliable alerting that caught issues before users did