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Practical AI: Machine Learning, Data Science, LLM: Creating tested, reliable AI applications
Manage episode 450035111 series 3506872
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It can be frustrating to get an AI application working amazingly well 80% of the time and failing miserably the other 20%. How can you close the gap and create something that you rely on? Chris and Daniel talk through this process, behavior testing, and the flow from prototype to production in this episode. They also talk a bit about the apparent slow down in the release of frontier models.
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Show Notes:
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章
1. Welcome to Practical AI (00:00:00)
2. Sponsor: Fly (00:00:57)
3. Thanksgiving preparations (00:03:33)
4. Agents in production (00:04:57)
5. AI ceiling & current hype (00:06:27)
6. Level of transformation (00:08:39)
7. Current models are mostly good enough (00:10:49)
8. Sponsor: Timescale (00:16:55)
9. Robust AI workflows (00:19:34)
10. Finding the right workflow (00:24:39)
11. Transition from notebook to code (00:30:47)
12. Sponsor: Eight Sleep (00:34:06)
13. Testing and integrating (00:36:44)
14. Sketching out a good framework (00:40:07)
15. Roles have shifted (00:47:23)
16. Outro (00:49:20)
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