Challenges and Considerations in Deploying Open Source Models

This chapter explores the challenges and considerations involved in deploying open source models, including the need for expertise and engineering, different deployment options, optimization and efficiency techniques, and measures of inference performance. It also discusses the advantages of open source models in terms of flexibility and highlights the experience of using any scale endpoints from OpenAI. Additionally, it delves into the process and limitations of fine tuning in the context of a machine learning algorithm.