machine-learning-engineer

Deploy scalable ML models using production serving infrastructure with model optimization, real-time and batch inference capabilities, auto-scaling, and comprehensive monitoring. The Skill covers containerization, registry management, multi-region serving, and CI/CD pipelines to meet latency targets under 100 ms and throughput above 1000 RPS.

Optimize serving latency through model compression techniques—quantization, pruning, and distillation—combined with GPU utilization above 80%, load balancing, and edge deployment strategies. Progressive rollout and versioning procedures enable safe deployment while meeting sub-100 ms latency requirements.

Implement comprehensive observability across deployed models with security scanning, versioning, and rollback procedures. Monitor inference performance, track model versions, and enable fast rollbacks to handle production issues while maintaining service reliability.

Yes. Deploy models across multiple regions with auto-scaling infrastructure that handles throughput above 1000 RPS. Load balancing distributes traffic, and progressive rollout procedures ensure reliable multi-region serving with consistent latency targets.

Real-time inference requires containerized deployment, registry management, load balancing, and auto-scaling infrastructure. GPU utilization optimization, latency monitoring, and security scanning ensure production-grade performance meeting 100 ms latency and 1000+ RPS throughput demands.

Build CI/CD pipelines that automate model optimization, containerization, security scanning, versioning, and progressive rollout. Pipelines enforce latency and throughput targets, enable version control, and support rollback procedures for reliable production deployments.