Essential Guide to LLMOps

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Reviewing LLMOps Components

In this chapter, we’ll dive into the components of LLMOps and how each piece enhances the efficiency, quality, and performance of the underlying LLMs. This chapter serves as a high-level overview that subsequent chapters will explore in depth. Our focus will be on the following areas and their impact:

• Data collection and preparation
• Model pre-training and fine-tuning
• Governance and review
• Inference, serving and scalability
• Monitoring
• Continuous improvement

Data collection and preparation

Data collection and preparation form the backbone of large language model (LLM) training and efficiency. This phase involves gathering, processing, and storing data in a manner that makes it most useful for training LLMs.

Data ...