AI is everywhere, part of the IT solutions we build and run today. Having an AI service, a good model, and data is not enough. As for cloud, the real difference is how we build, manage and run the whole solution. Microsoft created the Azure Well-Architected Framework for AI Workloads exactly for this reason — to help teams design AI systems that are reliable, secure, and cost-efficient.
The assessment has six main categories that we cover in the next section. Based on the results, we can gain a good understanding of the current AI workload estate and a list of actions to improve how you run and manage your AI workloads.
Designing the AI Application
The first step in building your AI application is to consider how you will structure it. Using containers for tasks like data processing or model inference helps maintain consistency across the system. This approach makes it easier to update, move, and manage different components. When you have multiple steps in your workflow, such as preparing data, calling models, and performing post-processing, an orchestrator can be very beneficial. Additionally, incorporating an API gateway allows you to manage all model endpoints in one centralised location. The main advantage of this setup is straightforward: the system operates more smoothly, scales more effectively, and is less likely to encounter issues.
Choosing the Application Platform
Selecting the right platform is very important. In Azure, you can choose from services like Machine Learning, Kubernetes, or serverless APIs. These make life easier because you don't need to manage infrastructure every day. You can use transient compute when required, save money, and still have high performance. When the platform fits the workload, everything becomes more stable and predictable.
Designing Training and Grounding Data
AI without good data is like an engine without fuel. You need to ensure your data is clean, secure, and unbiased. The framework helps you think about quality, access control, and retraining. For generative AI, properly grounded data, with indexing and chunking, ensures the model's answers are accurate and relevant. It means the AI becomes more trustworthy, and users get better results.
Building the Data Platform
Your data platform is the heart of everything. Azure provides many tools for collecting, transforming, and monitoring data. Automating these tasks saves time and avoids mistakes. Also, you can plan for reliability and cost control from the start. This makes your system stable and cost-effective.
MLOps and GenAIOps
This part is about making AI work like a machine, not a manual process. Automation pipelines, model monitoring, and repeatable deployments are key. You can update and improve models faster, without breaking production. It's how innovation becomes part of daily work, not just an experiment.
Operations, Testing, and Responsible AI
Good operations mean visibility. Dashboards, alerts, and proper documentation help teams react faster when something goes wrong. Testing your models and data regularly builds confidence. And finally, Responsible AI reminds us to be fair, secure, and transparent and to define clear roles so everyone knows their responsibility.
Why should you use it?
When you design, build and run a solution using the Azure Well Architecture approach, you don't just integrate AI. You align your AI components with the rest of your IT and cloud ecosystem.
It is perfect for companies that are looking to move their AI from PoC to production, by providing a strong foundation aligned with vendors' directions and tools.
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