Auquan is an AI startup that serves institutional investors and investment managers with curated news and documents to help them make better investment decisions.
In this presentation, I will discuss our approach for using a vector database and a tuned language model to curate news items at scale.
I will walk through the process and pitfalls for using these technologies in production, and provide best practices for achieving high performance. Specifically, I will discuss the metrics that we used for selecting a vector database and tune our language model.
ML Engineers, early stage Data Scientists
- How to use a vector database and a language model to curate news items at scale
- Best practices for using vector databases in production
- Pitfalls to avoid when using vector databases
- Introduction
- About Auquan
- Problem description
- Vector databases for news curation
- Choosing a vector database
- Using embeddings with vector databases for different tasks
- Offline population and real time inference
- Tuning a language model for news curation
- Tuning an LM
- Using tuned model for embedding
- Stack architecture
- Conclusion/QA
https://drive.google.com/file/d/1FubDUOYBJhzgaW-H6qxrlkEO49d3abDr/view?usp=sharing
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