Why Production-Ready AI Systems Matter More Than Ever

The gap between building a generative AI demo and deploying it in production has never been more apparent. Across Lithuania’s growing tech ecosystem, development teams are discovering that the skills needed to impress stakeholders with a prototype are vastly different from those required to run stable, secure, and scalable AI systems in the real world.

That’s why Europe Cloud is bringing our intensive “Generative AI in Production” workshop to Vilnius on March 26, 2026, at Workland Gedimino’s St. George Hall.

The Challenge: Moving Beyond the Notebook

If you’ve worked with Large Language Models (LLMs), you know the pattern: spinning up a Jupyter notebook, experimenting with prompts, getting impressive results, and thinking “this is ready for production.” Then reality hits.

How do you version your prompts?
How do you test for hallucinations at scale?
What happens when your model starts experiencing serving skew?
How do you prevent prompt injection attacks?
And perhaps most importantly—how do you keep costs under control when enterprise-level traffic hits your system?

These aren’t theoretical questions. They’re the everyday challenges faced by ML engineers and developers tasked with operationalizing generative AI applications.

What Makes This Workshop Different

This isn’t another introduction to AI or a high-level overview of generative models. This is a hands-on technical deep dive into GenAIOps—the emerging discipline of operationalizing Large Language Models at enterprise scale.

Real Labs. Real Infrastructure. Real Scenarios.

Every module in this full-day intensive includes practical labs using Google Cloud’s production-grade tools:

Morning Session: Managing Experimentation

• Build actual RAG (Retrieval-Augmented Generation) architectures
• Implement unit testing frameworks for GenAI applications
• Learn LLM evaluation techniques that catch issues before users do
• Track experiments systematically using Vertex AI

Afternoon Session: Productionization & Monitoring

• Deploy with Vertex AI Pipelines for scalable, versioned systems
• Implement safeguarding strategies against prompt injection
• Set up Cloud Logging for production LLM systems
• Monitor for evaluation-serving skew
• Establish continuous validation frameworks

From MLOps to GenAIOps: Understanding the Shift

Traditional MLOps practices don’t fully translate to generative AI. LLMs introduce unique operational challenges:

• Nondeterministic outputs requiring new testing strategies
• Prompt engineering as a critical operational concern
• Hallucination detection as an ongoing requirement
• Context window management for production systems
• Cost optimization without sacrificing quality

Our curriculum addresses these GenAI-specific operational challenges head-on.

Who Should Attend

This workshop is designed for technical practitioners who are:

• Developers building applications that leverage LLMs
• ML Engineers responsible for model deployment and maintenance
• Technical Leads planning GenAI implementation strategies
• Cloud Architects designing AI-powered systems

You should have basic familiarity with cloud concepts and programming experience. This is not a beginner course—it’s for professionals ready to bridge the gap between experimentation and production deployment.

Register here!

Why Vilnius? Why Now?

Lithuania’s tech sector is experiencing remarkable growth, with Vilnius emerging as a key innovation hub in Eastern Europe. As organizations across the Baltics accelerate generative AI adoption, the need for production-grade implementation skills has never been higher.

This workshop is part of Europe Cloud’s Spring 2026 training tour, following successful sessions in Sofia (GCP Core Infrastructure) and Riga (GenAI in Production). By bringing this training directly to Vilnius, Lithuanian tech professionals gain access to cutting-edge operational knowledge delivered at global standards.