“As Generative AI continues to evolve, enterprises are increasingly turning to Large language models to improve their business processes and customer experiences. They are becoming a key component of many NLP applications, such as chatbots, code generations etc.
However, many off-the-shelf large language models APIs have limitations and may not be suitable for enterprise needs due to domain specificity of certain use-cases or data sharing restrictions or for strategic reasons. Custom Language Models (LLMs) can be built to meet specific requirements and can be trained to perform specific tasks.
This talk will cover the benefits of building custom LLMs for enterprises, including the process of building custom LLMs, including data collection, preprocessing, training, and evaluation. We will discuss the different ways to adapt the open source LLMs for the enterprise needs.
Attendees will gain an understanding of how LLMs can be tailored to specific enterprise needs, and the benefits and challenges of building custom LLMs. We will also discuss how these custom LLMs can be scaled.
Key takeaways:
- Pros and Cons of building custom LLMs and what is realistically possible.
- Step-by-step demo of building custom LLMs for an example use case.
- Considerations for LLM size and latency trade-off.
- Best practices in efficiently scaling the custom LLMs.”
