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Found 53 Skills
Use when fine-tuning LLMs, training custom models, or optimizing model performance for specific tasks. Invoke for parameter-efficient methods, dataset preparation, or model adaptation.
Agent skill for data-ml-model - invoke with $agent-data-ml-model
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.
Expert in building scalable ML systems, from data pipelines and model training to production deployment and monitoring.
Deploy prompt-based Azure AI agents from YAML definitions to Azure AI Foundry projects. Use when users want to (1) create and deploy Azure AI agents, (2) set up Azure AI infrastructure, (3) deploy AI models to Azure, or (4) test deployed agents interactively. Handles authentication, RBAC, quotas, and deployment complexities automatically.
Expert in Machine Learning Operations bridging data science and DevOps. Use when building ML pipelines, model versioning, feature stores, or production ML serving. Triggers include "MLOps", "ML pipeline", "model deployment", "feature store", "model versioning", "ML monitoring", "Kubeflow", "MLflow".
Use when planning, debugging, tuning, evaluating, exporting, or deploying public Nemotron `embed`/`rerank` retrieval recipes.
Azure OpenAI Service 2025 models including GPT-5, GPT-4.1, reasoning models, and Azure AI Foundry integration
Expert knowledge for Azure AI Custom Vision development including best practices, decision making, limits & quotas, security, integrations & coding patterns, and deployment. Use when exporting Custom Vision models, calling prediction APIs, using ONNX/TensorFlow, managing CMK/RBAC, or Smart Labeler, and other Azure AI Custom Vision related development tasks. Not for Azure AI Vision (use azure-ai-vision), Azure AI services (use microsoft-foundry-tools), Azure Machine Learning (use azure-machine-learning), Azure AI Foundry Local (use microsoft-foundry-local).
Use when user needs ML model deployment, production serving infrastructure, optimization strategies, and real-time inference systems. Designs and implements scalable ML systems with focus on reliability and performance.
Use when user needs LLM system architecture, model deployment, optimization strategies, and production serving infrastructure. Designs scalable large language model applications with focus on performance, cost efficiency, and safety.
Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.