「deployment」の検索結果

Integrate Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodel, .mlpackage, .mlmodelc), predictions with auto-generated classes and MLFeatureProvider, compute unit configuration (CPU, GPU, Neural Engine), MLTensor, VNCoreMLRequest, MLComputePlan, multi-model pipelines, and deployment strategies. Use when loading Core ML models, making predictions, configuring compute units, or profiling model performance.

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。

Volcengine official documentation lookup helper. Supports both document search and full-content fetch across Volcengine products, developer tools, support content, best practices, pricing, deployment, troubleshooting, API, SDK, and policy pages.

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。

This skill should be used when the user asks to "deploy with OpenShip", "manage a self-hosted OpenShip instance", "debug an OpenShip deployment", or manage its domains, storage, backups, or upgrades through the CLI.

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。

This skill should be used when the user asks to "create openship.json", "configure an OpenShip deployment", "make a repo deployable on OpenShip", or fix "openship config validate" errors.

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, configure, troubleshoot, or review Cloudflare One Zero Trust and SASE deployments. Use cloudflare-one-migrations for migration planning from other vendors.

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。

Build, migrate, and deploy Next.js apps on Cloudflare Workers with vinext. Use when starting a Next.js project on Cloudflare, moving an existing app to Workers, choosing between vinext and OpenNext, or setting up vinext for Workers. For setup, migration, or deployment, install vinext's upstream skills with `npx skills add cloudflare/vinext` if missing, then read and follow the applicable skill and docs.

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 or troubleshoot Wrangler CLI commands and configure Worker projects for local development, Previews, deployment, and Cloudflare resource management.

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。

Deploys and operates a LiveKit agent in production: shipping a version to LiveKit Cloud and rolling it back, secrets and configuration, the worker process model and prewarming, safe async inside worker processes, provider timeouts and degradation, graceful shutdown, SDK upgrades, and observability. Use when the user says "deploy my agent", "roll back the deployment", "tail the agent logs", "the first call after a restart is slow", "attached to a different loop / event loop is closed", "prewarm the VAD", "shut down without dropping calls", "upgrade livekit-agents safely", "correlate logs by session", or is changing an agent codebase that is already live. Not for designing the agent (building-livekit-agents) or reproducing one bad conversation locally (debugging-livekit-agents).

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 AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or imported models rebuilt as dlnetwork for lean hardware, (2) direct C/C++ code generation from PyTorch and LiteRT models. Both patterns support all targets (Cortex-M/A/R, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, loadLiteRTModel, importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromTensorFlow, importNetworkFromKeras, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.

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。

Creates MATLAB interfaces to Python image processing and computer vision models from GitHub repositories or pip-installable packages using MPyReq. Use when asked to interface MATLAB with a Python CV/image model (segmentation, depth estimation, object detection, image generation, super-resolution, etc.), given a GitHub repo URL for an image/vision model, or asked to create an MPyReq demo for a deep-learning vision pipeline. Do NOT use for general-purpose Python-MATLAB interfacing, non-vision models (NLP, tabular, audio), model deployment/serving, or MATLAB-only image processing workflows.

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。

AWS CloudFormation infrastructure as code for stack management. Use when writing templates, deploying stacks, managing drift, troubleshooting deployments, or organizing infrastructure with nested stacks.

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。

Perform static application security testing with tools like Semgrep, CodeQL, and SonarQube. Identify security vulnerabilities in source code before deployment. Use when implementing secure SDLC, code review automation, or security gates in CI/CD pipelines.

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 frontend and full-stack apps on Vercel with previews, edge functions, environment promotion, and production guardrails. Use when shipping Next.js, SvelteKit, or static sites with zero-config CI/CD.

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。

Build reactive backends with Convex functions, schema validation, auth integration, and deployment workflows. Use when building real-time apps with type-safe server functions and automatic caching.

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。

Build and operate Retrieval-Augmented Generation (RAG) infrastructure with vector stores, embedding pipelines, and hybrid search. Covers ingestion, chunking strategies, reranking, and production deployment patterns.

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 local LLM workloads with Ollama, Open WebUI, and GPU-aware tuning for private development environments. Use when setting up private inference, local AI dev environments, or air-gapped LLM 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。

Implement Kubernetes security contexts, Pod Security Standards, and network policies. Secure cluster components and workloads. Use when hardening Kubernetes deployments or meeting security 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。

Harden AI/LLM deployments against prompt injection, data exfiltration, model theft, and supply chain attacks. Covers input validation, output filtering, access control, model API security, and compliance controls for production AI systems.

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。

Secure Docker images and container runtime configurations. Implement non-root users, read-only filesystems, and security contexts. Use when building secure container images or hardening container 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。

Build serverless applications on Azure Functions. Configure triggers, bindings, and deployment. Use when implementing serverless workloads on Azure.

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 Azure resources with ARM templates and Bicep. Create modular deployments and manage dependencies. Use when deploying Azure-native IaC.

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。

Administer MySQL/MariaDB databases. Configure replication and optimize performance. Use when managing MySQL 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。

Deploy ML models on Kubernetes with KServe (formerly KFServing) and NVIDIA Triton Inference Server. Includes canary deployments, autoscaling, model versioning, A/B testing, and GPU resource management for production model serving.

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 and secure company devices with MDM solutions — enroll macOS, Windows, iOS, and Android devices, enforce security policies, and automate software deployment. Use when setting up device management for a growing team.

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。