Results for “deployment”

Create and manage Kubernetes workloads with declarative YAML manifests, covering deployments, networking, security, storage, Helm charts, and troubleshooting.

Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow.

Guides securing and deploying the Dify platform in production, covering SSRF protection, code sandboxing, Docker/Kubernetes deployment, monitoring, and recovery.

Explains machine learning model predictions using SHAP values, covering feature importance, visualizations, debugging, bias analysis, and production deployment.

Deploys web apps with backend APIs, databases, and file storage to a public URL via the AppDeploy HTTP API.

Provides a sandboxed execution environment for running code snippets, automating tasks, and deploying logic across multiple languages with resource capping and network isolation.

Deploys applications, static sites, and edge functions to Azion using the Azion CLI, with preflight checks for authentication and build configuration.

Manage Cloudflare Tunnels via API and TypeScript SDK, including creation, configuration, DNS routes, and token-based deployment.

Configure a QQ official bot and connect it to OpenClaw AI, covering creation, IP whitelisting, deployment, and troubleshooting.

Next.js environment variable management with file precedence, variable types, and deployment configurations. Use when configuring Next.js applications, managing environment-specific settings, or deploying to Vercel/Railway/Heroku.

Specialized skill for building production-ready serverless applications on AWS. Covers Lambda functions, API Gateway, DynamoDB, SQS/SNS event-driven patterns, SAM/CDK deployment, and cold start optimization.

Specialized skill for building production-ready serverless applications on AWS. Covers Lambda functions, API Gateway, DynamoDB, SQS/SNS event-driven patterns, SAM/CDK deployment, and cold start optimization.

Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.

Database migration best practices for schema changes, data migrations, rollbacks, and zero-downtime deployments across PostgreSQL, MySQL, and common ORMs (Prisma, Drizzle, Kysely, Django, TypeORM, golang-migrate).

Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.

Design, organize, and manage Helm charts for templating and packaging Kubernetes applications with reusable configurations. Use when creating Helm charts, packaging Kubernetes applications, or implementing templated deployments.

Expert knowledge for Azure Blueprints development including troubleshooting, architecture & design patterns, security, configuration, and integrations & coding patterns. Use when defining Azure Blueprints, mapping built-in compliance sets, automating via CLI/PowerShell/REST, or fixing assignment errors, and other Azure Blueprints related development tasks. Not for Azure Policy (use azure-policy), Azure Resource Manager (use azure-resource-manager), Azure Managed Applications (use azure-managed-applications), Azure Deployment Environments (use azure-deployment-environments).

Expert knowledge for Azure Data Share development including troubleshooting, decision making, security, configuration, and deployment. Use when estimating Data Share costs, managing invitations/RBAC, cross-region deployments, dataset mapping, or automation, and other Azure Data Share related development tasks. Not for Azure Data Box (use azure-data-box-family), Azure Import Export (use azure-import-export), Azure Open Datasets (use azure-open-datasets), Azure Data Explorer (use azure-data-explorer).

Build production-ready systems with stability patterns: circuit breakers, bulkheads, timeouts, and retry logic. Use when the user mentions "production outage", "circuit breaker", "timeout strategy", "deployment pipeline", "chaos engineering", "bulkhead pattern", "retry with backoff", or "health checks". Also trigger when designing resilient microservices, planning zero-downtime deployments, or investigating cascading failure scenarios. Covers capacity planning, health checks, and anti-fragility patterns. For data systems, see ddia-systems. For system architecture, see system-design.

Analyze engineering delivery throughput, hiring funnel health, team structure, and production discipline for startup VPEs and founders.

Check and manage Azure quotas and service limits across resource providers and regions for deployment planning and capacity validation.

Manage Azure API Center resources including APIs, versions, definitions, deployments, and metadata using the Python management SDK.

Manage Azure API Center resources including services, workspaces, APIs, versions, definitions, environments, and deployments using the .NET SDK.

Deploy the AutoMagicCalib microservice and web UI from pre-built NGC release images using Docker Compose.

Design, configure, troubleshoot, and review Cloudflare One Zero Trust and SASE deployments across Access, Gateway, WARP, Tunnel, and DLP.

Design, deploy, and operate production-ready software with stability patterns, capacity planning, and zero-downtime releases.

Routes general Convex requests to the appropriate project skill, installs official Convex AI guidance, and provides command preflight instructions for Convex CLI operations.

Discovers, validates, or creates a SageMaker execution role before deploying or training, preventing IAM-related deployment failures.

Adopt Bun as a JavaScript runtime, package manager, bundler, and test runner, including migration from Node and Vercel deployment.

Coordinates five specialized agents (planning, research, code, review, deploy) into a pipeline that ships features from spec to production with automated gates.

Builds web APIs and full-stack apps with Hono, covering routing, middleware, validation, RPC clients, and deployment across edge runtimes.

Track ML experiments, manage the model registry with versioning, deploy models, and reproduce experiments using MLflow's framework-agnostic platform.

Implement Linkerd service mesh patterns for lightweight, security-focused service mesh deployments. Use when setting up Linkerd, configuring traffic policies, or implementing zero-trust networking with minimal overhead.

Design and implement ML operations — model registry, serving patterns, deployment strategies (shadow/canary/blue-green), drift detection, feature stores, retraining triggers, and prediction monitoring. Use when asked to "deploy a model", "model registry", "MLflow", "feature store", "drift detection", "retrain trigger", "shadow mode", "model versioning", "serving infrastructure", or "ML pipeline". Do NOT use for: prompt engineering or RAG pipelines — see prompt-engineering and rag-architect skills. Do NOT use for: general API deployment without an ML component.

Build, deploy, and modify GitHub Copilot SDK apps on Azure with scaffolding, model configuration, and deployment orchestration.

Secure Laravel applications with best practices for authentication, authorization, Eloquent safety, CSRF, XSS prevention, API security, and deployment configurations.