Designing and Deploying Reliable RAG Systems with MLOps
AI applications are evolving rapidly, with RAG systems becoming a key enabler for high-quality, context-aware solutions. But how do we bridge the gap between experimental RAG prototypes and reliable, scalable production systems?
Join us for a deep dive into MLOps and learn how to design, build, and operationalize RAG architectures — from concept to deployment. Language of the workshop: English.
Content
This workshop blends conceptual foundations with extensive hands-on sessions. You will gain both a strategic and practical understanding of how to design and manage robust RAG systems within an MLOps framework. Topics include:
- Conceptualizing and architecting high-quality RAG pipelines
- Setting up and automating the MLOps lifecycle for RAG systems
- Versioning and governance of datasets, embeddings, and retrieval indices
- Ensuring traceability through automated ML and data pipelines
- Experimenting with and comparing retriever–generator model variants
- Monitoring retrieval relevance and generation quality in production
Requirements
Laptop with internet access.
Target audience
This training is designed for engineers who want to conceptualize and implement RAG systems or extend their existing DevOps/MLOps processes to integrate advanced retrieval and generation components.
Conditions
At least 6 Participants are required for the workshop to take place.
Includes catering, documentation and a cluster in the cloud.
trainer
Rebecca HillerSoftware & Data Engineer
Designing and Deploying Reliable RAG Systems with MLOps
Join us for a deep dive into MLOps and learn how to design, build, and run RAG architectures — from concept to deployment.
Go to puzzle.chThis workshop is brought to you by Puzzle, a Swiss open-source company delivering practical, community-focused learning on modern cloud and infrastructure technologies.