Overview
How to Design a Secure & Scalable Azure AI Landing Zone (Real-World Guide)
In this video, we dive deep into building a production-ready Azure AI Landing Zone. Whether you are deploying Large Language Models (LLMs) or custom AI solutions, you need an infrastructure that is Secure, Scalable, and Reliable. This involves setting up a structured framework designed to accelerate your AI adoption, providing a secure and scalable environment for your workloads while adhering to best practices and compliance requirements.
What you will learn in this tutorial:
Security: Implementing Network Security and Identity (Microsoft Entra ID) for AI workloads, protecting sensitive data and models.
Scalability: How to leverage VM/AKS Clusters to handle massive compute demands, enabling efficient scaling for large-scale AI training and inference.
Reliability: Ensuring high availability and observability for mission-critical AI apps.
Governance: Setting up the right guardrails within your Data Landing Zone for compliance and optimization.
This architecture follows the Microsoft Cloud Adoption Framework (CAF) best practices to ensure your AI journey is enterprise-ready from day one.
Key Takeaways
- Practical cloud architecture patterns you can apply immediately
- Real-world implementation guidance from enterprise experience
- Azure, AWS, and multi-cloud considerations
- Security-first and cost-optimised design principles
Watch & Learn
Watch the full video above for a detailed walkthrough. Subscribe to Tech with RKM on YouTube for regular cloud and AI architecture content.
