The course focuses on Python workflows to manage scientific data through vector search, RAG and model serving.
Key topics
- Vector databases and semantic retrieval with Qdrant;
- RAG workflows using embedding and LLM APIs;
- Model serving with BentoML and integration into web applications.
Learning outcomes:
- Building and querying a vector database for scientific data;
Implementing a simple RAG pipeline;
Exposing and integrating a machine-learning model through an inference API.
