Cloud as a Story - Vunvulea Radu

 Working on legacy applications has always been intimidating. When I began a large migration of a .NET  project or an old Java system to Azure, it felt like going back in time to unravel years of decisions made by people who might no longer be there. Before you can write any code, you often spend days or weeks just figuring out how everything fits together. By the time you understand it all, it’s easy to feel your motivation slipping away. That’s why Microsoft’s new AI-powered tools are such a relief. With the latest GitHub Copilot and Azure features, modernization begins with clarity instead of worry. One major improvement is how the GitHub Copilot App Modernization Agent reviews legacy code. It highlights outdated frameworks, risky patterns, library issues, and migration risks. It’s like having a teammate who already understands the system. When I use it with Dr. Migrate, which checks the app on Azure’s side, I get a much clearer, faster view of what I’m dealing with. The ne...

AI’s true potential comes from advances in cloud platforms, not just from building better models.  Many AI projects run into problems because their cloud environments are not prepared to support them. In this article, I’ll share a practical guide on how organisations can move from a traditional cloud setup to an AI-Native platform in five clear steps. This process is based on what we see with clients whose cloud foundations are not ready for AI adoption. Let’s look at how these five steps can turn a basic cloud into a platform that learns, adapts, and grows. Step 1: Cloud-Native Refactoring Many organisations begin by lifting and shifting workloads into virtual machines (VMs) without changing how their applications are built. These apps still act like they’re running in a traditional data centre, with local data storage, slow scaling, tight dependencies, and all functions bundled together. When you add AI workloads, these systems often can’t keep up. Refactoring for the c...

  In technology conversations, “best practices” are mentioned everywhere—architecture reviews, governance frameworks, and delivery checklists. They are part of how we design and operate digital platforms. But in many projects, especially those with low or moderate workloads, best practices may feel theoretical. They look good on paper, yet the business impact is not always visible.   I recently worked on a project that challenged this perception. We pushed Azure Batch to operate at over 100,000 vCores, stretching the service's limits and placing significant pressure on Azure Storage, Azure Container Registry, and the networking layer. At this scale, every detail matters. And suddenly, all those Microsoft recommendations that previously seemed optional became essential.   1. Best Practices Deliver Real Value When Systems Become Truly Intensive For smaller systems or early-stage products, it is easy to overlook best practices. Everything works fine. For example: ...

For the last decade, the 6 Rs of cloud migration have been used to describe how enterprises should adopt the cloud: Rehost, Replatform, Refactor, Retain, and, sometimes, Retire. The 6 Rs of cloud migration have guided enterprises in adopting the cloud. However, with AI now central to digital transformation, these Rs alone are no longer sufficient. Cloud migration is just the first step; true AI-Native status requires a deeper cloud-native transformation. Customers labelling their migrations as Cloud-Native often have applications that still behave like on-premises systems, resulting in manual operations, static systems, and locked data that hinder AI programs. This is where a new perspective is required to build AI capabilities on top of the 6Rs. Pure cloud-native solutions are difficult for large enterprises. Realistically, we need to identify gaps and what is needed to prepare for AI integration. In the next part of the article, each R will be analysed in terms of AI-Native needs. R...