Build & Learn: From Data Science to AI Engineering Week 4

In der vierten Woche des Programms steht die Verbindung von Künstlicher Intelligenz mit eigenen Daten im Mittelpunkt. Es geht um die technische Umsetzung von RAG-Systemen, von der Dokumentenverarbeitung bis zur Implementierung von Vektordatenbanken wie pgvector. Die Teilnehmenden arbeiten an der Architektur moderner KI-Anwendungen. Themen sind das Erstellen von Embeddings, die Implementierung von Hybrid Search sowie die Qualitätsbewertung von Retrieval-Prozessen. Das Treffen findet in der Foodfactory am Berliner Hauptbahnhof statt. Wer mitmachen will, bringt den eigenen Laptop mit und arbeitet entweder am gemeinsamen Projekt oder an einer eigenen Anwendung.

We are moving to Luma, check out our future events there! 📅 Week 4: Connect AI to your data with RAG This event is part of our seven-week AI Engineering program, where we build an end-to-end AI application step by step and present our finished projects during Demo Day in Week 7. This week, we’ll work through the key layers of a modern AI application: * Learn how to chunk documents and create embeddings. * Use a vector database such as pgvector. * Build an ingestion pipeline to embed and store documents. * Implement similarity search and hybrid search. * Learn to evaluate retrieval quality and identify failure cases. By the end of the 7 week cycle, you should have a working AI application in your own GitHub repository and practical experience with AI pipelines, APIs, databases, retrieval, evaluation and deployment. You are welcome to follow the shared project or apply the weekly concepts to your own project. ✨ Who is this for? Anyone interested in transitioning towards AI engineering. No previous LLM or AI engineering experience is required. However, I recommend some basic programming experiences or be to help with your first build session. 💻 Bring: Your laptop and curiosity. Grab ☕ at the coffeeshop ​💬 Our slides and meeting link with be shared on our Discord → Join Discord Server