Essential guide to LLMOps : implementing effective LLMOps strategies and tools from data to deployment

Book Cover
Average Rating
Author
Doan, Ryan,
Published
Birmingham, UK : Packt Publishing Ltd., 2024.
Status
Available Online

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.

More Details

Format
Edition
1st edition.
Language
English
ISBN
1835887503, 9781835887509, 1835887511, 9781835887516

Notes

General Note
Includes index.
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.
Local note
O'Reilly O'Reilly Online Learning: Academic/Public Library Edition

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Citations

APA Citation, 7th Edition (style guide)

Doan, R. (2024). Essential guide to LLMOps: implementing effective LLMOps strategies and tools from data to deployment (1st edition.). Packt Publishing Ltd..

Chicago / Turabian - Author Date Citation, 17th Edition (style guide)

Doan, Ryan. 2024. Essential Guide to LLMOps: Implementing Effective LLMOps Strategies and Tools From Data to Deployment. Birmingham, UK: Packt Publishing Ltd.

Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)

Doan, Ryan. Essential Guide to LLMOps: Implementing Effective LLMOps Strategies and Tools From Data to Deployment Birmingham, UK: Packt Publishing Ltd, 2024.

Harvard Citation (style guide)

Doan, R. (2024). Essential guide to llmops: implementing effective llmops strategies and tools from data to deployment. 1st edn. Birmingham, UK: Packt Publishing Ltd.

MLA Citation, 9th Edition (style guide)

Doan, Ryan. Essential Guide to LLMOps: Implementing Effective LLMOps Strategies and Tools From Data to Deployment 1st edition., Packt Publishing Ltd., 2024.

Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.

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Grouped Work IDf882f99e-6443-88ff-4299-aecea3543d04-eng
Full titleessential guide to llmops implementing effective llmops strategies and tools from data to deployment
Authordoan ryan
Grouping Categorybook
Last Update2025-02-11 03:40:45AM
Last Indexed2025-05-22 03:46:29AM

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5050 |a Cover -- Copyright -- Contributors -- Table of Contents -- Preface -- Part 1: Foundations of LLMOps -- Chapter 1: Introduction to LLMs and LLMOps -- The evolution of NLP and LLMs -- The rise of machine learning in NLP -- Deep learning revolution -- The birth of LLMs -- Current state and future directions -- Traditional MLOps versus LLMOps -- Stages in the MLOps life cycle -- Specific challenges and methodologies in LLMOps -- Trends in LLM integration -- Integration of LLMs across industries -- Current trends and examples of LLM applications -- Core concepts of LLMOps
5058 |a Key LLMOps-specific terminology -- Model architecture -- LLMOps workflow overview -- Step-by-step overview -- Real-world example -- Summary -- Chapter 2: Reviewing LLMOps Components -- Data collection and preparation -- Data collection -- Processing raw text -- Tokenization -- Storing token ID mappings -- Dataset storage and database management systems (DBMSs) -- Model pre-training and fine-tuning -- Pre-training -- Fine-tuning -- Sliding windows -- Implementation of the sliding window technique -- Sliding window nuances -- Governance and review -- Avoiding training data leakage -- Access control
5058 |a Review -- Regulatory compliance -- Inference, serving, and scalability -- Online and batch inference -- CPU versus GPU serving -- Containerized deployments -- Monitoring -- Continuous improvement -- Summary -- Part 2: Tools and Strategies in LLMOps -- Chapter 3: Processing Data in LLMOps Tools -- Collecting data -- Collecting structured data -- Collecting semi-structured data -- Collecting unstructured data -- Transforming data -- Defining core data attributes -- Transforming data -- Preparing data -- Cleaning text data -- Handling insufficient context -- Transforming data for LLM consumption
5058 |a Example Workflow in PySpark -- Automating Spark Jobs -- Summary -- Chapter 4: Developing Models via LLMOps -- Creating features -- Tokenizing annotations -- Uniquely identifying tokens with attention masks -- Storing features -- Retrieving features -- Selecting the foundation model -- Choosing the LLM for your specific use case -- Testing foundation LLMs -- Addressing additional model concerns -- Fine-tuning the foundation LLM -- Tuning hyperparameters -- Automating model development -- Summary -- Chapter 5: LLMOps Review and Compliance -- Evaluating LLM performance metrics offline
5058 |a Evaluating binary, multi-class, and multi-label metrics -- Evaluating perplexity, BLUE, and ROUGE -- Evaluating reliability and robustness -- Evaluating conversational flow -- Securing and governing models with LLMOps -- Managing OWASP risks in LLMs -- Governance for LLMs -- Ensuring legal and regulatory compliance -- Operationalizing compliance and performance -- Operationalizing performance -- Security and governance -- Legal and regulatory compliance -- Validation of data and model licensing -- Human review points -- Summary -- Part 3: Advanced LLMOps Applications and Future Outlook
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