Whenever you are contemplating participating in @kaggle competitions and you might have heard someone say it is too far-fetched from practical data science work, consider this example: In the recent Science LLM competition participants learned among many other things: - How to fine-tune LLMs - How to properly use RAG techniques for augmenting LLMs - RAG chunking, embedding, similarity search, and other related techniques - Synthetic data generation for training models - How to optimize inference code for optimal runtime on limited HW resources. - How to fit large LLMs in small GPUs. People even managed to run 70B LLama2 on 2xT4. - And many other things I can encourage everyone to browse through the solution posts, lots of great insights there: kaggle.com/competitions/kagg…