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  <titleInfo>
    <title>Large language model-based solutions</title>
    <subTitle>how to deliver value with cost-effective generative AI applications</subTitle>
  </titleInfo>
  <name type="personal">
    <namePart>Subramanian, Shreyas</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
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    <role>
      <roleTerm type="text">author. </roleTerm>
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  <originInfo>
    <place>
      <placeTerm type="code" authority="marccountry">nju</placeTerm>
    </place>
    <place>
      <placeTerm type="text">Hoboken, New Jersey</placeTerm>
    </place>
    <publisher>John Wiley &amp; Sons Inc.</publisher>
    <dateIssued>c2024</dateIssued>
    <dateIssued encoding="marc">2024</dateIssued>
    <issuance>monographic</issuance>
  </originInfo>
  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
  </language>
  <physicalDescription>
    <extent>xvii, 190 pages :  illustrations (black and white) ; 23 cm.</extent>
  </physicalDescription>
  <abstract>"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 LLMsAssistance with the complexities of building and deploying affordable generative AI apps, including tuning and inference techniquesSelection 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." --Provided by th publisher </abstract>
  <tableOfContents>Contents: Introduction — Chapter 1 Introduction — Chapter 2 Tuning techniques for cost optimization — Chapter 3 Inference techniques for cost optimization — Chapter 4 Model selection and alternatives — Chapter 5 Infrastructure and deployment tuning strategies — Conclusion — Index.</tableOfContents>
  <targetAudience>Adult </targetAudience>
  <targetAudience authority="marctarget">adult</targetAudience>
  <note type="statement of responsibility">Shreyas Subramanian. </note>
  <note>Includes index. </note>
  <note>Purchased Ortega, Eric College of Computer Studies Computer Science</note>
  <note>Text in English </note>
  <subject>
    <topic>Large language models (Computer science)</topic>
  </subject>
  <subject>
    <topic>Generative artificial intelligence</topic>
  </subject>
  <subject>
    <topic>Application software</topic>
    <topic>Development</topic>
  </subject>
  <subject>
    <topic>Artificial intelligence</topic>
    <topic>Business applications</topic>
  </subject>
  <identifier type="isbn">9781394240722 [paperback]</identifier>
  <recordInfo>
    <recordContentSource authority="marcorg">University of Cebu-Banilad</recordContentSource>
    <recordCreationDate encoding="marc">260702</recordCreationDate>
    <recordChangeDate encoding="iso8601">20260702140958.0</recordChangeDate>
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