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 the deployment architecture of the LLM Twin, we will first look at three deployment types we can choose from: online real-time inference, asynchronous inference, and offline batch transform. Also, to better understand which option to choose for our LLM Twin use case, we will quickly walk you through a set of critical criteria we must consider before making an architectural decision, such as latency, throughput, data, and infrastructure. Also, we’ll weigh the pros and cons of monolithic and microservices architecture in model serving, a decision that can significantly influence the scalability and maintainability of your service.Once we’ve grasped the various design choices available, we’ll focus on understanding the deployment strategy for the LLM Twin’s inference pipeline. Subsequently, we will walk you through an end-to-end tutorial on deploying the LLM Twin service, including deploying our custom fine-tuned LLM to AWS SageMaker endpoints and implementing a FastAPI server as the central entry point for our users. We will then wrap up this chapter with a short discussion on autoscaling strategies and how to use them on SageMaker.

Hence, in this chapter, we will cover the following topics:

• Criteria for choosing deployment types
• Understanding inference deployment types
• Monolithic versus microservices architecture in model serving
• Exploring the LLM Twin’s inference pipeline deployment strategy
• Deploying the LLM Twin service
• Autoscaling capabilities to handle spikes in usage