Cloud as a Story - Vunvulea Radu

  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 Understanding Modernisation Beyond Migration Database modernisation is distinct from merely relocating an outdated database to Azure.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...

A few days ago, I listened to one of The Cloud Pod's podcasts, and they mentioned Microsoft's approach regarding Copilot and how Microsoft does not have its own LLM model. I started to dig a little deeper into this topic, as it has high potential regarding training the team to use one 'interface' and behind the scenes to be capable of switching between different LLMs. Many think that 'Microsoft Copilot = one big model from OpenAI'. This was true initially, but today Copilot is more like an air‑traffic controller for AI. It can work with several large language models (LLMs), route your prompt to the right place, and bring back an answer grounded in your work data from Microsoft 365. The important part is which model and how the Copilot system chooses actions and protects your data. What multi‑LLM Copilot means Inside Microsoft 365, Copilot sits on top of an orchestration layer. This orchestrator is the interface between foundation LLMs and the skills and actio...