| description | Use this agent when building production ML systems requiring model training pipelines, model serving infrastructure, performance optimization, and automated retraining. Specifically:\n\n<example>\nContext: A team needs to implement a complete ML system that trains a recommendation model, serves predictions at scale, and monitors for performance degradation.\nuser: "We need to build an ML pipeline that trains a collaborative filtering model on 100M user events daily, serves predictions sub-100ms, handles model drift, and automatically retrains when accuracy drops."\nassistant: "I'll architect the complete ML system with data validation pipeline, distributed training on multi-GPU infrastructure, model versioning, production serving with low-latency endpoints, and automated monitoring for prediction drift. I'll set up MLflow for experiment tracking, implement A/B testing for new model versions, and establish auto-retraining triggers with fallback mechanisms."\n<commentary>\nUse the ml-engineer agent when you need to build end-to-end ML systems from data validation through model serving, including infrastructure for handling production workloads, model governance, and continuous improvement.\n</commentary>\n</example>\n\n<example>\nContext: An existing ML service is experiencing latency issues and model degradation, requiring optimization of feature engineering and serving infrastructure.\nuser: "Our recommendation model has gone from 15ms to 150ms latency and accuracy dropped 3% last month. We need to optimize features, compress the model, and potentially switch to batch predictions."\nassistant: "I'll analyze the performance bottlenecks with profiling, identify feature engineering issues, implement online feature stores for faster lookups, apply model compression techniques like quantization, and potentially refactor to batch + caching patterns. I'll compare serving strategies (REST vs gRPC vs batch) and implement canary deployments for safe rollout."\n<commentary>\nInvoke this agent when addressing production ML system performance issues, model degradation, infrastructure bottlenecks, and optimization of existing deployed models.\n</commentary>\n</example>\n\n<example>\nContext: A data science team has a trained model and needs production deployment with monitoring, A/B testing capability, and auto-retraining infrastructure.\nuser: "We have a trained XGBoost model with 92% accuracy. How do we deploy this safely, test it against the current model, set up monitoring, and enable automatic retraining as new data arrives?"\nassistant: "I'll set up a production deployment pipeline using BentoML or Seldon, implement blue-green deployment for safe rollouts, configure A/B testing with traffic splitting and significance testing, establish monitoring dashboards for prediction drift and performance metrics, implement automated retraining triggers with DVC versioning, and set up rollback procedures."\n<commentary>\nUse this agent when you have a trained model ready for production and need to handle deployment, monitoring, testing, and operational aspects of maintaining ML systems in production.\n</commentary>\n</example> |
