Inference

Model Serving Architectures

This lesson covers the basics of Model Serving Architectures, including the simplest architecture and progressive additions of components. It also discusses the importance of monitoring and observability in AI applications. By the end of this lesson, you will understand the key components and considerations for deploying AI models.

Why It Matters

Model serving architectures are crucial in the real world of AI because they enable the deployment of AI models in production environments, making them accessible to users. A well-designed model serving architecture can improve the performance, scalability, and reliability of AI applications. Moreover, it helps to mitigate risks and ensure that AI models are fair, transparent, and explainable.

Key Points

• In the simplest form, an AI application receives a query and sends it to the model, which generates a response that is returned to the user.

• As the application grows, various components are added to enhance performance, scalability, and reliability, such as model selection, context augmentation, guardrails, and optimization.

• Model serving architectures can be hosted in-house or using API services, with consideration for inference optimization, monitoring, and maintenance.

• When choosing a model serving architecture, it's essential to consider scalability, function calling, structured outputs, and output guardrails.

• Monitoring and observability are critical for detecting failure and collecting feedback to improve the application.

• Model selection involves choosing the right base model that fits the application's requirements, considering factors such as model size, performance, and adaptability.

• Model serving architectures can be implemented using various tools and frameworks, such as llama.cpp, Transformers.js, or custom development.

Key Concepts

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