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Rp30.000
BUKU ELEKTRONIK LLM Engineer's Handbook: Master the art of engineering large language models from concept to production
Rp30.000
- Kondisi: Baru
- Min. Pemesanan: 1 Buah
- Etalase: Computers Information Technology
1.Ini adalah BUKU ELEKTRONIK WAJIB MENGGUNAKAN KURIR
2.File dibagikan lewat 3mail. Harap cantumkan alamat 3mail di CATATAN.
3.File full version PDF, EPUB, MOBI, AZW3, DJVU (Sesuai ketersediaan).
4.Request Buku hubungi kami melalui chat. Sebutkan JUDUL,EDISI,PENGARANG
5. Semua Transaksi WAJIB MELALUI TOKOPEDIA
Synopsis:
This LLM book provides practical insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps' best practices. It guides you through building an LLM-powered twin thats cost-effective, scalable, and modular, moving beyond isolated Jupyter Notebooks to focus on production-grade end-to-end systems. With a hands-on approach, the book covers essential topics such as data engineering, supervised fine-tuning, and deployment. Practical approach to building the LLM twin use case will help you implement MLOps components in your projects.
The book includes clear examples, AWS implementations, and best practices for bringing LLMs into production environments. If youre looking for a step-by-step guide, LLM Engineers Handbook by Paul Iusztin and Maxime Labonne is a must-read. Its beginner-friendly yet detailed enough for professionals, offering downloadable code, real AWS use cases, and practical insights into inference optimization, preference alignment, and real-time data processing. Whether you're integrating LLMs on the cloud or scaling them in production, this book enables you with the knowledge to succeed.
What you will learn
-Implement robust data pipelines and manage LLM training cycles
-Create your own LLM and refine with the help of hands-on examples
-Get started with LLMOps by diving into core MLOps principles like IaC
-Perform supervised fine-tuning and LLM evaluation
-Deploy end-to-end LLM solutions using AWS and other tools
-Explore continuous training, monitoring, and logic automation
-Learn about RAG ingestion as well as inference and feature pipelines
Table of Contents
-Understanding the LLM Twin Concept and Architecture
-Tooling and Installation
-Data Engineering
-RAG Feature Pipeline
-Supervised Fine-tuning
-Fine-tuning with Preference Alignment
-Evaluating LLMs
-Inference Optimization
-RAG Inference Pipeline
-Inference Pipeline Deployment
-MLOps and LLMOps
-Appendix: MLOps Principles
2.File dibagikan lewat 3mail. Harap cantumkan alamat 3mail di CATATAN.
3.File full version PDF, EPUB, MOBI, AZW3, DJVU (Sesuai ketersediaan).
4.Request Buku hubungi kami melalui chat. Sebutkan JUDUL,EDISI,PENGARANG
5. Semua Transaksi WAJIB MELALUI TOKOPEDIA
Synopsis:
This LLM book provides practical insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps' best practices. It guides you through building an LLM-powered twin thats cost-effective, scalable, and modular, moving beyond isolated Jupyter Notebooks to focus on production-grade end-to-end systems. With a hands-on approach, the book covers essential topics such as data engineering, supervised fine-tuning, and deployment. Practical approach to building the LLM twin use case will help you implement MLOps components in your projects.
The book includes clear examples, AWS implementations, and best practices for bringing LLMs into production environments. If youre looking for a step-by-step guide, LLM Engineers Handbook by Paul Iusztin and Maxime Labonne is a must-read. Its beginner-friendly yet detailed enough for professionals, offering downloadable code, real AWS use cases, and practical insights into inference optimization, preference alignment, and real-time data processing. Whether you're integrating LLMs on the cloud or scaling them in production, this book enables you with the knowledge to succeed.
What you will learn
-Implement robust data pipelines and manage LLM training cycles
-Create your own LLM and refine with the help of hands-on examples
-Get started with LLMOps by diving into core MLOps principles like IaC
-Perform supervised fine-tuning and LLM evaluation
-Deploy end-to-end LLM solutions using AWS and other tools
-Explore continuous training, monitoring, and logic automation
-Learn about RAG ingestion as well as inference and feature pipelines
Table of Contents
-Understanding the LLM Twin Concept and Architecture
-Tooling and Installation
-Data Engineering
-RAG Feature Pipeline
-Supervised Fine-tuning
-Fine-tuning with Preference Alignment
-Evaluating LLMs
-Inference Optimization
-RAG Inference Pipeline
-Inference Pipeline Deployment
-MLOps and LLMOps
-Appendix: MLOps Principles
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