Large Language Model Based Solutions
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Large Language Model-Based Solutions
Author: Shreyas Subramanian
language: en
Publisher: John Wiley & Sons
Release Date: 2024-04-02
Learn to build cost-effective apps using Large Language Models In Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI Applications, Principal Data Scientist at Amazon Web Services, Shreyas Subramanian, delivers a practical guide for developers and data scientists who wish to build and deploy cost-effective large language model (LLM)-based solutions. In the book, you'll find coverage of a wide range of key topics, including how to select a model, pre- and post-processing of data, prompt engineering, and instruction fine tuning. The author sheds light on techniques for optimizing inference, like model quantization and pruning, as well as different and affordable architectures for typical generative AI (GenAI) applications, including search systems, agent assists, and autonomous agents. You'll also find: Effective strategies to address the challenge of the high computational cost associated with LLMs Assistance with the complexities of building and deploying affordable generative AI apps, including tuning and inference techniques Selection criteria for choosing a model, with particular consideration given to compact, nimble, and domain-specific models Perfect for developers and data scientists interested in deploying foundational models, or business leaders planning to scale out their use of GenAI, Large Language Model-Based Solutions will also benefit project leaders and managers, technical support staff, and administrators with an interest or stake in the subject.
Theory and Practice of Quality Assurance for Machine Learning Systems
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The Routledge Handbook of the Translation Industry
The Routledge Handbook of the Translation Industry provides an accessible and comprehensive overview of current and emerging practices, workflows, and processes in the translation industry, the professional and socio-economic contexts in which industry actors operate, and best practices in translation industry teaching and research in this rapidly developing field. Comprising 33 chapters from scholarly and industry voices, this handbook addresses the many issues arising from growing technologisation, new trends in translation procurement and production, and increasing pressures on the range of actors in the translation industry. The content spans both bottom-up and top-down perspectives, using a variety of theoretical, praxiological, and data-driven approaches. In addition to providing coverage of a range of well-established professional profiles, workflows, and resources, this handbook adopts a novel approach in addressing emerging topics such as global sustainable development and wellbeing, economics, the platform economy, and social media and the influencer economy. The opening mapping chapter and final roundtable chapter provide fitting conceptual and forward-looking bookends for the content. This handbook offers a topical and much-needed forum to engage with the challenges and opportunities facing the translation industry and constitutes an essential point of reference for students and researchers of translation as well as industry practitioners and professionals.