13 skills found
Export a promoted fine-tuned model in the right deployment format — merged safetensors, LoRA-only, GGUF with imatrix, or FP8. Use after a checkpoint passes promotion, when choosing a quantization format for a target device, or when an exported model fails its smoke test.
Start, query, and stop a network-specific TAO inference microservice ({network_arch}-inference-microservice) by delegating container execution to the appropriate platform skill. Handles container image resolution, job-payload JSON construction, and the service registry. Use when the user wants to run inference on a TAO model checkpoint using a microservice container, deploy a TAO inference endpoint, or stop a running inference container.
CLIP vision-language model for image-text retrieval, zero-shot classification, embedding extraction, ONNX export, and TensorRT deployment. Use when fine-tuning or training CLIP, running zero-shot classification, computing image embeddings, or deploying CLIP to ONNX/TensorRT.
Build, deploy, modify GitHub Copilot SDK apps on Azure. MANDATORY when codebase contains @github/copilot-sdk or CopilotClient — use this skill instead of azure-prepare. PREFER OVER azure-prepare when codebase contains copilot-sdk markers. WHEN: copilot SDK, @github/copilot-sdk, copilot-powered app, deploy copilot app, add feature, modify copilot app, BYOM, bring your own model, CopilotClient, createSession, sendAndWait, azd init copilot. DO NOT USE FOR: general web apps without copilot SDK (use
データ準備からモデル学習、検証、本番デプロイメントまでのエンドツーエンドMLOpsパイプラインを構築します。MLパイプラインの作成、MLOpsプラクティスの実装、またはモデル学習とデプロイメントワークフローの自動化時に使用します。
Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform
Model Registry Manager - Auto-activating skill for ML Deployment.Triggers on: model registry manager, model registry managerPart of the ML Deployment skill category.
This skill enables AI assistant to deploy machine learning models to production environments. it automates the deployment workflow, implements best practices for serving models, optimizes performance, and handles potential errors. use this skill when th... Use when deploying or managing infrastructure. Trigger with phrases like 'deploy', 'infrastructure', or 'CI/CD'.
Build automated machine learning pipelines with feature engineering, model selection, and hyperparameter tuning.Use when automating ML workflows from data preparation through model deployment.Trigger with phrases like "build automl pipeline", "automate ml workflow", or "create automated training pipeline".
World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.
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.
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
