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  • Book Overview & Buying LLMs in Enterprise
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LLMs in Enterprise

LLMs in Enterprise

By : Ahmed Menshawy, Mahmoud Fahmy
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LLMs in Enterprise

LLMs in Enterprise

By: Ahmed Menshawy, Mahmoud Fahmy

Overview of this book

The integration of large language models (LLMs) into enterprise applications is transforming how businesses use AI to drive smarter decisions and efficient operations. LLMs in Enterprise is your practical guide to bringing these capabilities into real-world business contexts. It demystifies the complexities of LLM deployment and provides a structured approach for enhancing decision-making and operational efficiency with AI. Starting with an introduction to the foundational concepts, the book swiftly moves on to hands-on applications focusing on real-world challenges and solutions. You’ll master data strategies and explore design patterns that streamline the optimization and deployment of LLMs in enterprise environments. From fine-tuning techniques to advanced inferencing patterns, the book equips you with a toolkit for solving complex challenges and driving AI-led innovation in business processes. By the end of this book, you’ll have a solid grasp of key LLM design patterns and how to apply them to enhance the performance and scalability of your generative AI solutions. *Email sign-up and proof of purchase required
Table of Contents (20 chapters)
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Part 1: Background and Foundational Concepts
7
Part 2: Advanced Design Patterns and Techniques
13
Part 3: GenAI in the Enterprise
19
Index

Data standardization and normalization

Data standardization involves rescaling the features so that they have a mean of zero and a standard deviation of one. This ensures that features are on the same scale, making it easier for models to learn patterns without being biased toward certain features. On the other hand, normalization typically rescales features to a range, such as [0, 1], which is useful when features have different units or magnitudes. Both techniques help prevent some features from dominating others during training and contribute to improved model performance.

Tokenization techniques

One of the key components of preparing data for LLMs is tokenization. Tokenization refers to breaking down text into smaller units, such as words or subwords, which can then be processed by the model. Different tokenization techniques are used depending on the nature of the model and the language being processed. For example, BERT uses a WordPiece tokenizer, which splits words...

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LLMs in Enterprise
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