Engineering teams are building new ML-embedded products in days leveraging the power of large language models (LLMs). But after they experience the cool “aha” moment of seeing the response from an OpenAI API, teams often realize they have two unfulfilled needs:
- Dependent on an external service: Organizations have sensitive data, or latency requirements, that prohibit them from being able to use a large general-purpose public model like OpenAI.
- Customizing LLMs for specific tasks: Organizations don’t need general solutions; they need to leverage the power of LLMs to solve a particular task.
Now there’s a better way to use and finetune LLMs on your own data that is faster, cheaper, and doesn’t require giving away any proprietary data. Join this session and live demo to learn how open-source Ludwig, the declarative ML framework created at Uber, along with Predibase makes it possible to:
- Use and finetune best-in-class LLMs—like GPT, LLaMa, and Bloom—on your own data without giving it away
- Shrink LLMs to handle specific ML tasks saving thousands of dollars on inference
- And, best of all, build an entire LLM pipeline in a few minutes with just a few lines of a YAML configuration
