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  • Format: ePub

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…mehr

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Produktbeschreibung
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.


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Autorenporträt
Ahmed Menshawy is the Vice President of AI Engineering at Mastercard. He leads the AI Engineering team to drive the development and operationalization of AI products, address a broad range of challenges and technical debts for ML pipelines deployment. He also leads a team dedicated to creating several AI accelerators and capabilities, including serving engines and feature stores, aimed at enhancing various aspects of AI engineering.