Essential Guide to LLMOps: Implementing effective strategies for Large Language Models in deployment and continuous improvement by Ryan Doan
English | July 31, 2024 | ISBN: 1835887503 | 190 pages | MOBI | 1.21 Mb
Unlock the secrets to mastering LLMOps with innovative approaches to streamline AI workflows, improve model efficiency, and ensure robust scalability, revolutionizing your language model operations from start to finish
Key FeaturesGain a comprehensive understanding of LLMOps, from data handling to model governanceLeverage tools for efficient LLM lifecycle management, from development to maintenanceDiscover real-world examples of industry cutting-edge trends in generative AI operationPurchase of the print or Kindle book includes a free PDF eBookBook Description
The rapid advancements in large language models (LLMs) bring significant challenges in deployment, maintenance, and scalability. This Essential Guide to LLMOps provides practical solutions and strategies to overcome these challenges, ensuring seamless integration and the optimization of LLMs in real-world applications.
This book takes you through the historical background, core concepts, and essential tools for data analysis, model development, deployment, maintenance, and governance. You'll learn how to streamline workflows, enhance efficiency in LLMOps processes, employ LLMOps tools for precise model fine-tuning, and address the critical aspects of model review and governance. You'll also get to grips with the practices and performance considerations that are necessary for the responsible development and deployment of LLMs. The book equips you with insights into model inference, scalability, and continuous improvement, and shows you how to implement these in real-world applications.
By the end of this book, you'll have learned the nuances of LLMOps, including effective deployment strategies, scalability solutions, and continuous improvement techniques, equipping you to stay ahead in the dynamic world of AI.
What you will learnUnderstand the evolution and impact of LLMs in AIDifferentiate between LLMOps and traditional MLOpsUtilize LLMOps tools for data analysis, preparation, and fine-tuningMaster strategies for model development, deployment, and improvementImplement techniques for model inference, serving, and scalabilityIntegrate human-in-the-loop strategies for refining LLM outputsGrasp the forefront of emerging technologies and practices in LLMOpsWho this book is for
This book is for machine learning professionals, data scientists, ML engineers, and AI leaders interested in LLMOps. It is particularly valuable for those developing, deploying, and managing LLMs, as well as academics and students looking to deepen their understanding of the latest AI and machine learning trends. Professionals in tech companies and research institutions, as well as anyone with foundational knowledge of machine learning will find this resource invaluable for advancing their skills in LLMOps.
Table of ContentsIntroduction to LLMs and LLMOpsReviewing LLMOps ComponentsProcessing Data in LLMOps ToolsDeveloping Models via LLMOpsLLMOps Review and ComplianceLLMOps Strategies for Inference, Serving, and ScalabilityLLMOps Monitoring and Continuous ImprovementThe Future of LLMOps and Emerging Technologies
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