Inference Pipeline Deployment

Deploying the inference pipeline for the large language model (LLM) Twin application is a critical stage in the machine learning (ML) application life cycle. It’s where the most value is added to your business, making your models accessible to your end users. However, successfully deploying AI models can be challenging, as the models require expensive computing power and access to up-to-date features to run the inference. To overcome these constraints, it’s crucial to carefully design your deployment strategy. This ensures that it meets the application’s requirements, such as latency, throughput, and costs. As we work with LLMs, we must consider the inference optimization techniques presented in Chapter 8, such as model quantization. Also, to automate the deployment processes, we must leverage MLOps best practices, such as model registries that version and share our models across our infrastructure.

To understand how to design...