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 It highlights outdated frameworks, risky patterns, library issues, and migration risks.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...

In the last decade, enterprises migrated thousands of workloads to the cloud for elasticity and lower infrastructure costs. Nevertheless, most workloads behave as they did on-premises: tightly coupled, batch-oriented, and blind to data in motion. This creates high virtualised technical debt with few systems ready for AI. IT leaders should identify and prioritize refactoring critical applications. Early steps include adopting containerization, implementing DevOps, and exploring data integration for real-time flow — establishing an agile, AI-ready ecosystem. This growing intent to re-architect for AI highlights a critical gap between cloud adoption and true cloud modernisation. To better understand what is getting in the way, it’s important to recognize a fundamental insight: Migrations ≠ Modernisations , where Migration offers quick lift-and-shift cloud hosting, but not the value of true Modernisation. From Lift & Shift to AI-Native Moving your servers from a data center to the clou...

  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...

  This week, Cluj-Napoca hosted he 13 th edition of ITDays . With over 800 participants and more than 70 speakers, the two-day conference brought together IT specialists from the area. It was a valuable networking opportunity, allowing us to renew existing relationships and connect with new people. The most notable appearance was that of Morgan Stanley, which joined ITDays as one of its sponsors. With a large delivery office in Budapest, they aroused the interest of many people at the conference. Besides networking, good coffee and chatting with extraordinary people, I had the opportunity to deliver a session about AI-native applications inside the cloud. I presented a five-step playbook for preparing cloud environments and businesses for AI adoption—covering modernisation, data readiness, scalability, governance, and continuous innovation to unlock intelligence and agility. Thank you, Ovidiu, and the rest of the team, for making this conference possible!  

  When companies transition to the cloud, they typically begin with applications and virtual machines, which is often the easier part of the process. The actual complexity arises later when databases are moved. To save time and effort, cloud adoption is more of a cloud migration in an IaaS manner, fulfilling current, but not future needs. Even organisations that are already in the cloud find that their databases, although “migrated,” are not genuinely modernised. This disparity becomes particularly evident when they begin to explore AI technologies. Understanding Modernisation Beyond Migration Database modernisation is distinct from merely relocating an outdated database to Azure. It's about making your data layer ready for future needs, like automation, real-time analytics, and AI capabilities. AI needs high throughput, which can be achieved using native DB cloud capabilities. When your database runs in a traditional setup (even hosted in the cloud), in that case, you will enc...

AI requires data that is accessible, organized , governed, and secure . When storage is disorganized or exposed to the internet, AI becomes slow, expensive, and prone to risks . In contrast, when storage is private, structured, and well-described with metadata, AI operates faster, is more cost-effective, and maintains compliance . This focus is not just on sophisticated models ; it centers on how we store and transfer data . The foundation shapes the outcomes. AI alters the risk profile by consuming data rapidly and broadly . It is advisable to treat storage as a private system by default, regularly discover sensitive data, and integrate these insights into your indexing rules . The use of Private Endpoints, combined with Defender for Storage for malware scanning, and applying immutability provides the basic security feature for Azure Storage .   It’s important to implement them from day zero and not to delay them . It is more cost-effective to implement them fr...