「ai deployment」の検索結果

Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, create agent, update agent, add tool to agent, invoke agent, agent.yaml, agent insights, pull agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, prompt optimizer, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).

日本語の概要は準備中です。原文の説明を表示しています。

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).

日本語の概要は準備中です。原文の説明を表示しています。

Expert knowledge for Azure Container Instances development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, and deployment. Use when configuring ACI standby pools, static IP networking, GPU containers, secure ACR pulls, or GitHub Actions deploys, and other Azure Container Instances related development tasks. Not for Azure Container Apps (use azure-container-apps), Azure Kubernetes Service (AKS) (use azure-kubernetes-service), Azure App Service (use azure-app-service), Azure Virtual Machines (use azure-virtual-machines).

日本語の概要は準備中です。原文の説明を表示しています。

Rollback procedures, risk assessment, pre/post-deployment validation, and contingency planning. Load when orchestrating deployment or preparing rollback plans. For deployment strategy details (canary, blue-green, rolling), see `cicd-and-deployment` skill.

日本語の概要は準備中です。原文の説明を表示しています。

Debug Fastly Compute deployments that appear successful but return stale/wrong responses. Use when: (1) fastly compute publish succeeds but version check shows old version, (2) new endpoints return 404 after deployment, (3) cache-busted requests work but regular requests fail, (4) fastly domain list doesn't show your custom domain. Covers edge propagation timing, cached error responses, and domain management API differences.

日本語の概要は準備中です。原文の説明を表示しています。

Apache Airflow DAG Patterns workflow skill. Use this skill when the user needs Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

日本語の概要は準備中です。原文の説明を表示しています。

Apache Airflow DAG Patterns workflow skill. Use this skill when the user needs Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

日本語の概要は準備中です。原文の説明を表示しています。

Kolla-Ansible deployment engine skill for containerized OpenStack lifecycle management. Use when bootstrapping, deploying, reconfiguring, or upgrading OpenStack environments. Covers globals.yml configuration, inventory management, container operations, rolling upgrades, password management, and troubleshooting deployment failures across all service containers. This is the meta-skill that manages the deployment lifecycle for all OpenStack services.

日本語の概要は準備中です。原文の説明を表示しています。

Kolla-Ansible day-2 operations skill for post-deployment infrastructure lifecycle management. Covers service reconfiguration (globals.yml changes, config overrides, prechecks, targeted reconfigure with --tags), minor and major OpenStack upgrades (image pull, upgrade procedure, rollback), container management (restart, logs, health inspection), maintenance mode (compute disable, instance drain, host maintenance), password rotation, certificate renewal, and rolling updates. This skill is for operations after initial deployment -- the kolla-ansible deployment skill covers initial bootstrap and deploy.

日本語の概要は準備中です。原文の説明を表示しています。

Orchestrate end-to-end machine learning pipelines using Prefect or Airflow with DAG construction, task dependencies, retry logic, scheduling, monitoring, and integration with MLflow, DVC, and feature stores for production ML workflows. Use when automating multi-step ML workflows from data ingestion to deployment, scheduling periodic model retraining, coordinating distributed training tasks, or managing retry logic and failure recovery across pipeline stages.

日本語の概要は準備中です。原文の説明を表示しています。

Deploy a Next.js application to Vercel. Covers project linking, environment variables, preview deployments, custom domains, and production deployment configuration. Use when deploying a Next.js app for the first time, setting up preview deployments for pull requests, configuring custom domains, or managing environment variables in a production Vercel deployment.

日本語の概要は準備中です。原文の説明を表示しています。

Design AI model deployment strategies with rollout. TRIGGERS - Use when user needs help with ai-deployment-strategy related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Design canary deployment strategies for AI. TRIGGERS - Use when user needs help with ai-canary-deployment related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Run a pre and post-deployment checklist. Use when the user says "deploy to production", "deployment checklist", "release checklist", "pre-deploy checks", "is this ready to deploy", "deployment readiness", "reduce deployment risk", "change management", or is about to ship code to production and wants to reduce failure risk - even if they don't explicitly say "deployment checklist".

日本語の概要は準備中です。原文の説明を表示しています。

Diagnoses GKE JobSet interruptions, restarts, and preemptions for AI/ML training workloads autonomously. Use when troubleshooting JobSet restart loops, spot VM preemptions, node readiness failures, host VM issues, or coordinator worker crashes. Don't use for general GKE cluster creation, basic workload deployment, or non-JobSet application issues.

日本語の概要は準備中です。原文の説明を表示しています。

Use when configuring org-wide platform settings: MFA enforcement, My Domain setup and deployment, session timeout and security settings, password policies, trusted IP ranges (Network Access), and CSP Trusted Sites. Owns these settings as deployable Settings metadata (Security.settings, MyDomain.settings, Company.settings, Language.settings) and the order in which a new org is stood up. Trigger keywords: 'MFA setup', 'My Domain', 'session settings', 'password policy', 'trusted IP ranges', 'CSP trusted sites', 'Security.settings', 'SecuritySettings metadata', 'MyDomainSettings', 'settings-meta.xml', 'enhanced domains', 'deploy org settings', 'new org setup order', 'sessionTimeout enum', 'lockSessionsToIp', 'enableAdminLoginAsAnyUser'. NOT for user-level login restrictions (use user-management), NOT for permission model design (use permission-sets-vs-profiles), NOT for security audit and hardening review (use security/org-hardening-and-baseline-config).

日本語の概要は準備中です。原文の説明を表示しています。

Use when authoring, reviewing, or maintaining Salesforce DevOps operational documents — runbooks, environment matrices, deployment guides, and post-deploy validation checklists. Triggers: 'runbook', 'environment matrix', 'deployment guide', 'pre-deploy checklist', 'post-deploy validation', 'how do I document a deployment', 'rollback procedure template'. NOT for what actually belongs on the pre-deploy gate — use devops/pre-deployment-checklist. NOT for release calendar, versioning, or go/no-go governance — use devops/release-management. Also covers: the devops-process YAML artefact (environment ladder, change-request state machine, deploy contract, deploy order, RACI), the deployOptions the runbook must record (checkOnly, testLevel, rollbackOnError, ignoreWarnings, purgeOnDelete, singlePackage, runTests), destructiveChangesPre/Post ordering, and the change-request emergency path.

日本語の概要は準備中です。原文の説明を表示しています。

Stop AI agents from secretly bypassing your rules. Mechanical enforcement with git hooks, secret detection, deployment verification, and import registries. Born from real production incidents: server crashes, token leaks, code rewrites. Works with Claude Code, Clawdbot, Cursor. Install once, enforce forever.

日本語の概要は準備中です。原文の説明を表示しています。

Safety, privacy, cost management, and frank advice for building always-on AI agents with episodic memory. Covers data hygiene, privacy risk surfaces, runaway cost prevention, scope creep, psychological effects of persistent AI companions, and responsible deployment patterns. This is the skill that tells you what can go wrong and how to prevent it. Activate on: "agent safety", "always-on agent privacy", "agent cost control", "persistent agent risks", "AI companion safety", "agent data hygiene", "runaway agent costs", "parasocial AI risk", "/always-on-agent-safety". NOT for: architecture design (use always-on-agent-architecture), input design (use always-on-agent-inputs), application brainstorming (use always-on-agent-applications), healthcare compliance specifically (use hipaa-compliance).

日本語の概要は準備中です。原文の説明を表示しています。

Azure AI Projects SDK for .NET. High-level client for Azure AI Foundry projects including agents, connections, datasets, deployments, evaluations, and indexes. Use for AI Foundry project management, versioned agents, and orchestration. Triggers: "AI Projects", "AIProjectClient", "Foundry project"...

日本語の概要は準備中です。原文の説明を表示しています。

Manage Railway projects, services, deployments, environments, variables, and deployment logs via Railway's public GraphQL API — one endpoint, relay-style pagination, two token flavors (account vs project), typed mutations for deploys and env vars. Use when the user wants to deploy, check service status, manage env vars, or read deployment logs programmatically without the Railway MCP installed.

日本語の概要は準備中です。原文の説明を表示しています。

Harden AI/LLM deployments against prompt injection, data exfiltration, model theft, and supply chain attacks.

日本語の概要は準備中です。原文の説明を表示しています。

Use when working with Azure Resource Manager — azure Resource Manager (ARM) template and deployment management. Covers template validation, deployment operations, resource group inspection, what-if analysis, deployment history, and template spec management. Use when deploying ARM templates, reviewing deployment status, analyzing resource groups, or troubleshooting failed deployments.

日本語の概要は準備中です。原文の説明を表示しています。

Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.

日本語の概要は準備中です。原文の説明を表示しています。