Generative models: exploration to deployment

Practical AI: Machine Learning, Data Science, LLM

What is the model lifecycle like for experimenting with and then deploying generative AI models? Although there are some similarities, this lifecycle differs somewhat from previous data science practices in that models are typically not trained from scratch (or even fine-tuned). Chris and Daniel give a high level overview in this effort and discuss model optimization and serving.

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Featuring:

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

Show Notes:

• BigDL
• Article: Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA
• Previous episode: Running large models on CPUs
• Baseten’s Truss
• Seldon
• Hugging Face’s TGI
• Intel Gaudi 2
• Intel TDX

Something missing or broken? PRs welcome!

Chapitres

1. Welcome to Practical AI (00:00:07)

2. Daniel at GopherCon & IIC (00:00:43)

3. Local inference & TDX (00:03:31)

4. Cloudflare Workers AI (00:08:23)

5. Implementing new models (00:09:43)

6. Sponsor: Neo4j (00:16:14)

https://neo4j.com/nodes

7. Navigating HuggingFace (00:17:11)

8. Model Sizes (00:20:21)

9. Running the model (00:24:34)

10. Model optimization (00:30:20)

11. Cloud vs local (00:34:17)

12. Cloud standardization (00:39:26)

13. Open source go-to tools (00:43:00)

14. Keep trying! (00:46:21)

15. Outro (00:48:18)