Clearly being able to call out where there is an issue in your RAG system is hard and tricky. W&B RAG++ course simplifies it for you. Excellent work from Bharat and Ayush for clear explanation and sharing good quality code to understand.
Great
Awsome course for beginner
Awsome course
Key takeaways for me are Ingestion pitfalls, Query enhancement best practices and Agentic RAG
Thank you This Course was really well done.
If I can give a small suggestion, maybe more videos. But I like the straight to the point approach. It was super helpful for my master thesis!
Great so far!
Though I have yet to complete the course, I commend you guys at WANDB. This was easy and interesting. However, to beginners, it might be too rushed and fast. Overall, I enjoyed the course. Kudos to your team.
Very broad view on many levers to increase RAG performances.
And grounded with concrete examples and notebooks to apply these technics... I highly recommend !
머신 러닝 모델을 프로덕션에 적용하려면 여러 복잡한 구성 요소로 이루어진 수명 주기를 끊임없이 반복해야 하므로 까다로운 작업이 될 수 있습니다. 속도와 정확도를 구현하고, 프로덕션에 즉시 사용할 수 있는 ML 모델과 서비스를 지속적으로 제공하는 엔드 투 엔드 머신 러닝 파이프라인을 구축하려면 체계적이고 유연하며 협업적인 프로세스, 즉 효율적인 MLOps 시스템을 갖추는 것이 중요합니다.
