Deploy Production Azure Infrastructure in 2 Days
A command-driven AI interface walks you through environment design. Claude handles the troubleshooting and wiring in real time. You deploy dev, staging, and prod environments that are compliant with Microsoft Cloud Adoption Framework and built entirely on Azure Verified Modules.
No cloud architect required. No Terraform expertise needed. Bring a laptop, VS Code, and Claude Code. Azure environments are provided.
Watch the Training in Action
See what the AI-orchestrated deployment methodology looks like before you book a seat.
What You Do in Two Days
You show up with a laptop, VS Code, and Claude Code. Azure training environments are provided. No credentials to configure, no subscription to provision. Over two days, you deploy three complete landing zone environments (development, staging, production) using a command-driven interface with Claude AI assisting via chat or voice. Each environment gets progressively more hardened. You validate every deployment before moving to the next one. You break something on purpose and fix it. You walk out understanding the architecture because you wired it yourself.
The real product isn't the environments. It's the methodology. You learn how to work through AI as your execution layer while you stay in the decision layer. You learn where to trust it and where to stop it. You take home the config files, the validation templates, and the method to replicate it in your own environment tomorrow.
The Messy Middle
Discussion: why manual deployment fails, why full CI/CD takes months, and what sits between them. This is the gap you're here to fill.
Discovery-First Rule
Real incident: what happens when AI assumes instead of verifies. You run discovery commands against your own subscription and see what AI needs to know before it touches anything.
Config Structure
Walk through tfvars design. Why flat. Why environment drives everything. Why AVM-first. You understand the architecture decisions before deployment starts.
The Wizard
You run /deploy for dev. Seven questions. Watch it build in real time. Your first environment is live.
Expected Results
You create expected results for your dev deployment BEFORE running validation. This is the methodology's core: define what "correct" looks like before you check.
/validate
Run the full validation suite against your live dev landing zone. See pass/fail per component. Green checks and red flags against your own infrastructure.
Break Something On Purpose
Instructor introduces a deliberate misconfiguration. You use /troubleshoot to diagnose and fix it. This is where you learn what AI recovery actually looks like.
/draw
Query Resource Graph, generate a Mermaid architecture diagram of everything you deployed. You take home a visual artifact of your work.
End of Day 1 you have:
- A working dev landing zone (VNet, subnets, NSGs, Key Vault, Log Analytics, App Insights, managed identity, policy assignments)
- An expected results document you wrote yourself
- A validation report showing pass/fail per component
- An architecture diagram of your environment
- Understanding of why discovery comes before deployment
Environment Progression
Review how staging builds on dev. Not just "bigger VMs" but additive layers of infrastructure. You understand why environments are different, not just how.
Staging Deploy + Validate
Run /deploy for staging. Same test structure, different expected values. More VM SKUs, additional integrations. You see the pattern repeating.
Conversation-Gate Pattern
Claude refuses to proceed to prod until staging validates clean. The conversation itself is the deployment gate. This is the trust boundary in action.
Prod Deploy
Full hardened deployment: enforced policies, SSH-only access, purge protection, 90-day log retention. Production-grade from the start.
Lunch Workshop (30-45 min): Wire a VM into Monitoring
Hands-on mini-lab. You install Azure Monitor Agent on your dev VM, configure a Data Collection Rule, and push logs into Log Analytics. Verify data flowing with KQL. Practical skill you use immediately in the afternoon and take home knowing how to do.
Prod Validation
Run /validate against prod. See enforced policies, SSH-only auth, purge protection all verified. Your production environment passes its own compliance check.
AI-Assisted RCA
Use Claude to run root cause analysis across your resource group. Query Log Analytics with KQL, pull Activity Logs, correlate events. Instead of pre-written tests, ask AI to inspect a live environment and surface anomalies. You see what's possible when AI reads real telemetry.
/draw All Three
Run /draw against dev, staging, and prod. Compare three architecture diagrams side by side. Visual proof of the additive layer model you built over two days.
Wrap-Up
How to build your own wizard for your environment. Discussion of Course 2 (policy + drift). Q&A.
End of Day 2 you have:
- Three working landing zones (dev, staging, prod)
- Three validation reports showing environment-specific compliance
- Azure Monitor Agent installed and pushing real logs
- AI-generated RCA report from live telemetry
- Three architecture diagrams showing environment progression
- The method to do this again in your own environment
What You Walk Away With
The Method
- How to think about AI as an execution layer while staying in the decision layer
- Where the trust boundary is between human judgment and AI action
- How to design validation before deployment, not after
- How to recognize when AI goes wrong before it costs you
- A framework for conversation-gated progression that prevents mistakes from cascading
The Files
- JSON configuration files for dev, staging, and prod environments
- Markdown expected results templates you can modify for your own infrastructure
- Pre-deploy test guides showing how to write assertions before running terraform apply
- Architecture diagrams of everything you built
- Validation report templates
The Confidence
- Three working environments you deployed yourself
- The experience of breaking something on purpose and fixing it with AI
- Real understanding of what AVM modules do and why they matter
- Hands-on monitoring configuration you can replicate tomorrow
These files aren't locked to the training environment. You modify them for your own subscriptions and use them immediately. That's the point. You're paying for knowledge and confidence, same as any Red Hat or Microsoft training engagement.
Prerequisites
- A laptop (Windows, Mac, or Linux)
- VS Code installed
- An active Claude Code subscription
- No Azure subscription needed. Training environments are provided
- No Terraform expertise required
- No AI/ML experience required
Claude Code is the same tool you'll use in production after the training. Every workflow, every command, every validation pattern you learn here works the same way on your own infrastructure tomorrow. That's the investment: you're not learning a training-only tool, you're building fluency with your production toolkit.
Who This Is For
Engineering teams without a dedicated cloud architect.
You need compliant environments now. Not in three months after a pipeline buildout.
Teams building on Azure but stuck between portal and CI/CD.
You know clickops doesn't scale. You're not ready for full GitOps. This is the bridge.
Technical leaders evaluating AI for infrastructure.
You need to see it work safely before approving it for your org. This is the proof.
MSPs onboarding new Azure customers.
You need to stand up environments fast and repeatably. This teaches the methodology, not just the tool.
Anyone tired of hearing "you can't trust AI in production."
You can. This training shows you where the line is.
Pricing
- Full 2-day hands-on workshop
- All deployment configuration files
- Expected results templates and validation guides
- Architecture diagrams of everything built
- 30-day post-training email support for questions
- Access to future course material updates
Enterprise-Grade from Day One
These aren't guidelines. They're enforced by the deployment system. Every environment you build meets enterprise compliance requirements from the start.
Microsoft Cloud Adoption Framework
All landing zone architecture follows Microsoft's official CAF guidance. Enforced by the deployment system, not suggested.
Azure Verified Modules
All Terraform modules are Microsoft-maintained and validated. Not community modules. Not custom code that breaks on the next provider update.
Front9/Back9 Naming Convention
Mathematical naming that prevents collisions at scale. Integrated into all deployment tooling. Zero lookup tables, fully deterministic.
Ready to learn how to work through AI, not beside it?
Enrollment open. Reach out to discuss fit for your team.
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