Fine-tuning vs RAG

Practical AI: Machine Learning, Data Science, LLM

In this episode we welcome back our good friend Demetrios from the MLOps Community to discuss fine-tuning vs. retrieval augmented generation. Along the way, we also chat about OpenAI Enterprise, results from the MLOps Community LLM survey, and the orchestration and evaluation of generative AI workloads.

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Featuring:

• Demetrios Brinkmann – Twitter
• Chris Benson – Twitter, GitHub, LinkedIn, Website
• Daniel Whitenack – Twitter, GitHub, Website

Show Notes:

• MLOps Community
• LLM survey report
• LLMs in Production Event - Part III

Something missing or broken? PRs welcome!

Chapitres

1. Welcome to Practical AI (00:00:07)

2. Practical AI & Friends (00:00:43)

3. Look into MLOps community (00:02:01)

4. Changes in the AI community (00:04:19)

5. Finding the norm (00:07:34)

6. Matching models & uses (00:08:30)

7. Stages of debugging (00:11:21)

8. Layer orchestration (00:13:10)

9. Practical hot takes (00:16:26)

10. Fine-tuning is more work (00:21:46)

11. Retrieval augmented generation (00:24:13)

12. MLOps survey (00:31:23)

13. The next survey (00:38:09)

14. Enterprise hypetrain (00:41:50)

15. OpenAI & your data (00:42:39)

16. AI vendor lock-in? (00:43:19)

17. Now what do we do? (00:47:44)

18. Hype in the AI life (00:48:58)

19. Goodbye (00:56:35)

20. Outro (00:57:23)