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  <titleInfo>
    <title>Natural Language Processing on Oracle Cloud Infrastructure</title>
    <subTitle>Building Transformer-Based NLP Solutions Using Oracle AI and Hugging Face</subTitle>
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    <namePart>Assoudi, Hicham.</namePart>
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    <dateIssued encoding="marc">2024</dateIssued>
    <edition>1st ed. 2024.</edition>
    <issuance>monographic</issuance>
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    <extent>XX, 402 p. 190 illus. online resource.</extent>
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  <abstract>This book demonstrates how to use Oracle Cloud Infrastructure (OCI) and Hugging Face technologies to develop advanced NLP solutions. Through a practical case study, it addresses common NLP challenges and offers strategies for creating efficient, cost-effective transformer-based models. By the end of this book, you will have the skills and knowledge to create cutting-edge NLP solutions on OCI, customized to meet the needs of various industries and projects. The book takes you through the complete NLP solution life cycle—covering data preparation, model fine-tuning, deployment, and monitoring—while highlighting key topics such as cost-effectiveness and responsible AI for NLP implementations. Drawing from real-world experience and offering practical insights, it bridges the gap between theory and practice, equipping you to design and deploy scalable, cost-efficient NLP solutions. What You Will Learn Master key NLP concepts and the OCI ecosystem Create high-quality datasets using Hugging Face and OCI Data Labeling Service Fine-tune domain-specific pre-trained models from Hugging Face using OCI Data Science Notebook Sessions Deploy and operationalize your models with OCI Data Science Model Deployments Automate the NLP life cycle with OCI Data Science Pipelines Implement cost-effective strategies throughout the entire NLP life cycle, from dataset preparation to model training and deployment .</abstract>
  <tableOfContents>Part 1: Foundations and Case Study Introduction -- Chapter 1: NLP Essentials -- Chapter 2: Oracle Cloud for NLP -- Chapter 3: Healthcare NLP Case Study -- Part2: Case Study Implementation -- Chapter 4: Tenancy Preparation -- Chapter 5: Dataset Preparation -- Chapter 6: Model Fine-tuning -- Part 3: Case Study Deployment and Wrap-Up -- Chapter 7: Model Deployment and Monitoring -- Chapter 8: MLOps and Conclusion.</tableOfContents>
  <note type="statement of responsibility">by Hicham Assoudi.</note>
  <subject authority="lcsh">
    <topic>Artificial intelligence</topic>
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    <topic>Machine learning</topic>
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    <topic>Cloud Computing</topic>
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  <subject authority="lcsh">
    <topic>Python (Computer program language)</topic>
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  <subject>
    <topic>Artificial Intelligence</topic>
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  <subject>
    <topic>Machine Learning</topic>
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  <subject>
    <topic>Cloud Computing</topic>
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  <identifier type="isbn">9798868810732</identifier>
  <identifier type="uri">https://doi.org/10.1007/979-8-8688-1073-2</identifier>
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