canary-deployment
>
Works with
---
name: canary-deployment
description: >
license: MIT
---
# Canary Deployment
## Table of Contents
- [Overview](#overview)
- [When to Use](#when-to-use)
- [Quick Start](#quick-start)
- [Reference Guides](#reference-guides)
- [Best Practices](#best-practices)
## Overview
Deploy new versions gradually to a small percentage of users, monitor metrics for issues, and automatically rollback or proceed based on predefined thresholds.
## When to Use
- Low-risk gradual rollouts
- Real-world testing with live traffic
- Automatic rollback on errors
- User impact minimization
- A/B testing integration
- Metrics-driven deployments
- High-traffic services
## Quick Start
Minimal working example:
```yaml
# canary-deployment-istio.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: myapp-v1
namespace: production
spec:
replicas: 3
selector:
matchLabels:
app: myapp
version: v1
template:
metadata:
labels:
app: myapp
version: v1
spec:
containers:
- name: myapp
image: myrepo/myapp:1.0.0
ports:
- containerPort: 8080
---
// ... (see reference guides for full implementation)
```
## Reference Guides
Detailed implementations in the `references/` directory:
| Guide | Contents |
|---|---|
| [Istio-based Canary Deployment](references/istio-based-canary-deployment.md) | Istio-based Canary Deployment |
| [Kubernetes Native Canary Script](references/kubernetes-native-canary-script.md) | Kubernetes Native Canary Script |
| [Metrics-Based Canary Analysis](references/metrics-based-canary-analysis.md) | Metrics-Based Canary Analysis |
| [Automated Canary Promotion](references/automated-canary-promotion.md) | Automated Canary Promotion |
## Best Practices
### ✅ DO
- Follow established patterns and conventions
- Write clean, maintainable code
- Add appropriate documentation
- Test thoroughly before deploying
### ❌ DON'T
- Skip testing or validation
- Ignore error handling
- Hard-code configuration valuesMore Deployment & CI/CD skills
azure-enterprise-infra-planner
microsoft/azure-skills
Architect and provision enterprise Azure infrastructure from workload descriptions. For cloud architects and platform engineers planning networking, identity, security, compliance, and multi-resource topologies with WAF alignment. Generates Bicep or Terraform directly (no azd). WHEN: 'plan Azure infrastructure', 'architect Azure landing zone', 'design hub-spoke network', 'plan multi-region DR topology', 'set up VNets firewalls and private endpoints', 'subscription-scope Bicep deployment', 'Azure Backup for VM workloads'. PREFER azure-prepare FOR app-centric workflows.
azure-kubernetes-app-deploy
microsoft/azure-skills
Use when deploying an existing web application or API to an already-running Azure Kubernetes Service cluster. Detects the framework, generates a Dockerfile and Kubernetes manifests, validates against AKS Deployment Safeguards, and deploys with verification. WHEN: deploy app to AKS, deploy to existing AKS cluster, containerize app for Kubernetes, generate K8s manifests for Azure, set up CI/CD for AKS, my AKS deployment is failing safeguard checks, I have a Django/Express/Spring Boot app to run on AKS. DO NOT USE FOR: creating or provisioning an AKS cluster (use azure-kubernetes), assessing migration to AKS Automatic (use azure-kubernetes-automatic-readiness), or deploying to non-AKS targets like Web Apps, Container Apps, or Functions.
finetuning
microsoft/azure-skills
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).

