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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 building high-throughput data pipelines and the production ML infrastructure that sits between raw data and product decisions.
- Streaming pipelines (Kafka, Flink) processing millions of events a day
- Batch orchestration (Airflow, dbt) keeping warehouse models fresh and tested
- ML serving: feature stores, model registries, and low-latency inference endpoints
- Monitoring and alerting with Prometheus and Grafana, with SLOs we actually page on
Outcome
Measurable results
- Data freshness for product analytics stayed under a minute
- Warehouse models had automated test coverage
- Inference endpoints held p99 latency under 100ms
- Alerting caught issues before users noticed them