Large language models have gained a substantial amount of attention in the area of AI and machine learning. While they are impressive, there are many applications where they are not the best option. In this episode Piero Molino explains how declarative ML approaches allow you to make the best use of the available tools across use cases and data formats.
- Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
- Your host is Tobias Macey and today I'm interviewing Piero Molino about the application of declarative ML in a world being dominated by large language models
- How did you get involved in machine learning?
- Can you start by summarizing your perspective on the effect that LLMs are having on the AI/ML industry?
- In a world where LLMs are being applied to a growing variety of use cases, what are the capabilities that they still lack?
- How does declarative ML help to address those shortcomings?
- The majority of current hype is about commercial models (e.g. GPT-4). Can you summarize the current state of the ecosystem for open source LLMs?
- For teams who are investing in ML/AI capabilities, what are the sources of platform risk for LLMs?
- What are the comparative benefits of using a declarative ML approach?
- What are the most interesting, innovative, or unexpected ways that you have seen LLMs used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on declarative ML in the age of LLMs?
- When is an LLM the wrong choice?
- What do you have planned for the future of declarative ML and Predibase?
- Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
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- From your perspective, what is the biggest barrier to adoption of machine learning today?
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