Cloud as a Story - Vunvulea Radu

When we talk about Artificial Intelligence today, it feels like a revolution. Everyone wants to try it, but the reality is hard: studies show that up to 80% of AI projects never reach production. Often, the reason is not the model itself but the team behind it. Too many companies start with a clever prototype and then get stuck, unable to deploy or monitor at scale. This is where Microsoft Azure, together with the right team shape, makes the difference. More Than a Model: A Full Cloud AI Solution An AI solution in the cloud is never just a model. It is a complete ecosystem of applications, infrastructure, data pipelines, and security. On Azure, we can connect all of this. Azure Machine Learning gives us model lifecycle management, Azure OpenAI Service brings natural language power, and Cognitive Services add vision or speech. This runs on a secure, automated, and scalable cloud infrastructure. For business leaders, this means faster time to market and real outcomes, not just pilots. Fo...

  Cluj just hosted DevTalks , one of the biggest and most vibrant tech events in Transylvania. The event brought together thousands of developers, engineers, and innovators under one roof, and the energy was fantastic. I had the chance to take the stage and talk about something very close to my heart: how to build and structure teams for Cloud AI projects. We often talk about the technology — the models, the platforms, the automation — but what really makes or breaks success is the team. My message was simple: building AI in the cloud isn’t a one-person show. It takes AI/ML experts, data scientists, cloud and platform engineers, developers, and security specialists all working side by side. The real magic happens when you get the balance right and put automation and platform engineering at the core If I had to leave the audience with one idea, it’s this: “AI in the cloud is a team sport. When you get the structure right, innovation, scalability, and cost efficiency follow natur...

In March 2025, I had the opportunity to be a speaker at the Conf42 conference. The session that I delivered covered the cloud architecture that needs to be build to manage the payload generated by AI.   Refs: https://www.conf42.com/Cloud_Native_2025_Radu_Vunvulea_data_infrastructure_algorithms

Together with the Endava Cloud team, I had the opportunity to participate in Cloud Fest 2025, held at Europa Park in Germany. Cloud Fest focuses on cloud technology from all fields, with a high focus on private and public cloud. The session covered topics such as AI, open-source, security, sustainability, and the evolution of the cloud. Numerous showcases provided us with the opportunity to network and gain a better understanding of the EU market.   My primary goal for the event was to network and visit all the exhibitors. Yes, I visited all the exhibitors, and I had the opportunity to chat with around 120 of them. There was a mix of cloud hardware providers, solution providers for private cloud, physical and virtual appliance providers and startups from this space. I was surprised that no public cloud vendors were present at the conference, considering there were over 150 exhibitors and more than 9,000 attendees. The cloud repatriation trend in the EU is stronger than ever. T...

On March 13th, I had the opportunity to speak at Azure AI Connect about modern AI architectures.  My session focused on the importance of modernizing cloud systems to efficiently handle the increasing payload generated by AI.

AI is reshaping the way we build and run businesses in all industries. One challenge with AI models is scaling. Traditional infrastructure is not built for the dynamic scaling required by AI models during training or operation.   To optimise cost and reduce operational overhead, a modern approach combining serverless and microservices to provide a flexible, scalable, and efficient workload layer is required. Microsoft Azure enables these two mechanisms through Azure Functions and Azure Kubernetes Services. Serveless is required for AI deployments, especially because of unpredictable demands. The capability of running a function triggered by an AI agent in response to an event without the overhead of deployments is crucial for multi-agent AI solutions. Serverless is needed for real-time image recognition, language translation and dynamic execution of payloads triggered by APIs, data streams and IoT devices. Another advantage of a serverless approach is agility and the abilit...