What is technical debt, and how does it apply to large language models? We dive into a really interesting conversation that goes from technical debt into system and code maintenance, which is probably a much better way to think about the challenges we have in maintaining the infrastructure systems, code, data and data lakes that we have to deal with on an everyday basis.

How do we maintain, store, track and update the LLMs themselves? How do we know and manage which model is being used when we retire a model?

References:
https://www.linkedin.com/pulse/how-google-measures-manages-tech-debt-abi-noda/
https://devops.com/are-llms-leading-devops-into-a-tech-debt-trap/

Transcript: https://otter.ai/u/ngUClgtMmLLKXCFalDxFNsNdVr4?utm_source=copy_url
Image: https://www.pexels.com/photo/anonymous-worker-in-heavy-duty-gloves-3846440/

Ver todos los episodios