Summary
Taking a trained LLM from research into real-world production involves navigating many complex challenges around aspects such as scalability, monitoring, and unintended behaviors. Responsibly deploying capable, reliable models involves diligent planning around scalability, interpretability, testing, and monitoring. Techniques such as fine-tuning, safety interventions, and defensive design enable us to develop applications that produce helpful, harmless, and readable outputs. With care and preparation, generative AI holds immense potential benefit to industries from medicine to education.
We’ve delved into deployment and the tools used for deployment. Particularly, we deployed applications with FastAPI and Ray. In earlier chapters, we used Streamlit. There are many more tools we could have explored, for example, the recently emerged LangServe, which is developed with LangChain applications in mind. While it’s still relatively fresh, it’s definitely...
