Exact public version
senior-ml-engineer v1
ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization. Use when the user asks about deploying ML models to production, setting up MLOps infrastructure (MLflow, Kubeflow, Kubernetes, Docker), monitoring model performance or drift, building RAG pipelines, or integrating LLM APIs with retry logic and cost controls. Focused on production and operational concerns rather than model research or initial training.
d59113e47741e9732e1bd9174aad3ea69d4fb853b2ece61eb157f131933c0e7cPackage contents
Manifest
ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization. Use when the user asks about deploying ML models to production, setting up MLOps infrastructure (MLflow, Kubeflow, Kubernetes, Docker), monitoring model performance or drift, building RAG pipelines, or integrating LLM APIs with retry logic and cost controls. Focused on production and operational concerns rather than model research or initial training.
23c0cbb93b9d8ccb535188758446f96bcc10bf5b7b1753ac4e1f73137637fdfb- SKILL.md9,796 bytes
- references/llm_integration_guide.md8,900 bytes
- references/mlops_production_patterns.md7,307 bytes
- references/rag_system_architecture.md10,286 bytes
- scripts/ml_monitoring_suite.py2,782 bytes
- scripts/model_deployment_pipeline.py2,812 bytes
- scripts/rag_system_builder.py2,777 bytes