Applying Declarative ML Techniques To Large Language Models For Better Results
from AI Engineering Podcast Hosted by Tobias Macey
Summary 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. Announcements 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 Interview Introduction 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? Contact Info LinkedIn Website Closing Announcements 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. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Links Predibase Podcast Episode Ludwig Podcast.__init__ Episode Recommender Systems Information Retrieval Vector Database Transformer Model BERT Context Windows LLAMA The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
Embed this episode
Chapters
- 0:10 Introduction and Guest Introduction
- 0:48 Piero Molino's Journey into Machine Learning
- 3:02 Impact of Large Language Models on AI and ML
- 6:05 Pitfalls and Challenges of LLMs
- 9:34 Open Source Models vs. Commercial LLMs
- 12:39 Declarative ML and Simplifying LLM Integration
- 15:29 Comparing Declarative ML and LLMs
- 18:14 Transformer Models vs. LLMs
- 21:20 Multimodal Use Cases for ML
- 23:25 Context Window Limitations in LLMs
- 27:12 Addressing Hallucinations in LLMs
- 30:27 Bias and Inclusivity in LLMs
- 34:49 Innovative Applications of Declarative ML and LLMs
- 37:02 Lessons Learned in Declarative ML
- 39:03 When LLMs Are the Wrong Choice
- 41:15 Future Plans for Predibase and Declarative ML
- 44:19 Closing Remarks and Contact Information
No transcript yet.