Building AI apps is no longer just about calling an API—it’s about designing a complete system from idea to production.
In this session, we’ll walk through the end-to-end journey of creating a YouTube Summarizer powered by Gemini using Vertex AI. You’ll learn how to take a simple concept and transform it into a scalable, production-ready AI application.
We’ll cover how to extract video content, process it using advanced language models, and generate meaningful summaries that enhance user experience. More importantly, we’ll focus on how to architect, deploy, and scale the solution using modern cloud tools.
What You’ll Learn
- How to design an AI-powered product from scratch
- Using Gemini models via Vertex AI for text summarization
- Extracting and processing YouTube video data
- Structuring backend workflows for AI pipelines
- Deployment strategies for scalable AI applications
- Best practices for performance, cost, and reliability
Key Highlights
- End-to-end system design (Idea → Build → Deploy)
- Real-world implementation approach
- Scalable architecture using cloud services
- Practical insights for production-ready AI apps
Who Should Attend
- Android & Mobile Developers
- Backend & Full Stack Developers
- Developers exploring AI + Cloud integration
- Anyone interested in building real-world AI applications
By the end of this session, you’ll have a clear roadmap to go from idea to deployment, and the confidence to build your own AI-powered applications using Gemini and Vertex AI
