What you'll learn
Building production level applications using open source AI tools
Understand the role of open-source AI technologies in building real-world AI products.
Build AI-powered features using open-source models, APIs, vector databases, orchestration tools, and deployment frameworks.
Why you should attend
AI is moving quickly from experimentation to real products. This course helps you go beyond demos and prototypes by showing you how to build reliable AI products using open-source technologies.
You should attend this course if you want to understand how to design, build, evaluate, and deploy AI-powered applications that can work in real production environments. You will learn how to choose the right open-source models and tools, structure AI workflows, improve reliability, reduce hallucinations, monitor performance, and handle practical challenges such as latency, cost, scalability, and privacy.
By attending, you will gain hands-on knowledge of modern AI product development and leave with a clearer path for turning open-source AI technologies into useful, scalable, and production-ready solutions.
More about hosts & guests
Amr is a software engineer at AWS, based in Berlin. 8+ years across frontend and full-stack, these days mostly shipping production systems that have to stand up to real teams and real traffic, not just look good in isolation.
He's also the founder of Tadween, an AI-powered platform that turns messy career histories into structured, credible profiles. Building Tadween took him deep into the stack most people are still trying to decode: custom MCP servers, multi-agent orchestration, LLM-driven document pipelines. He's built it, debugged it, and has strong opinions about which parts actually matter and which parts are hype.
His teaching style follows from that, no framework evangelism, no buzzword chasing. Just the mental models that survive contact with real systems.
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