Cloud as a Story - Vunvulea Radu

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...