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Book Overview & Buying
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Table Of Contents
LLMs in Enterprise
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This chapter highlighted the importance of robust data strategies in training and optimizing LLMs. It covered methods for acquiring and preparing high-quality data, emphasizing its role in enhancing model performance and fairness. Key topics included ensuring data quality, leveraging augmentation and enrichment techniques, and addressing challenges such as bias and scalability. These strategies form the foundation for the effective and ethical use of LLMs in real-world applications.
Having obtained a good data strategy for training and fine-tuning LLMs, the second critical step in the LLM life cycle is deployment to production environments. In the next chapter, we’ll examine how to bridge theoretical model capabilities with real-world, practical applications. You’ll learn about the technical, regulatory, and operational factors for enterprise LLM deployments, including techniques for managing latency and throughput to meet business needs. The chapter will...