Large language models (LLMs) are rapidly transforming how software is built, delivered, and operated. Practice of Research and Development Efficiency Driven by Large Models provides a comprehensive and practice-oriented guide to applying LLMs across the full DevOps lifecycle from development and testing to operations, security, and project management.
Starting with the foundations of large language models, Transformers, and GPT architectures, you will progresses to advanced topics such as fine-tuning techniques (LoRA, QLoRA, PEFT), retrieval-augmented generation (RAG), and agent-based systems. You will then see how these technologies can be applied in real-world DevOps scenarios, including intelligent operations, automated testing, code generation, incident analysis, and project delivery optimization.
With extensive case studies drawn from enterprise environments, this book bridges theory and practice, helping you improve R&D efficiency, automation, reliability, and decision-making using large language models.

Zobacz także:

• Microsoft Azure. Kurs video. Zostań administratorem systemów IT 169,00 zł, (76,05 zł -55%)
• Amazon Web Services (AWS). Kurs video. Zostań administratorem systemów IT 199,00 zł, (89,55 zł -55%)
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• Projektowanie system 79,00 zł, (41,87 zł -47%)

Spis treści

AI-Powered DevOps with LLMs. Applying Large Language Models to Software Delivery and SRE eBook -- spis treści

• 1. Introduction to Large Language Models
• 2. The Cornerstone of Large Language Models—Transformer
• 3. From Transformer to ChatGPT33
• 4. Fine-Tuning Techniques for Large Language Models
• 5. Enterprise AI Application Technology— RAG
• 6. Three Foundational Pillars of Software Delivery
• 7. Practical Applications of Large Language Models in Operations Scenarios
• 8. Practical Applications of Large Language Models in Testing Scenarios
• 9. Practical Applications of Large Language Models in Programming Scenarios
• 10. Practical Applications of Large Language Models in Project Management Scenarios
• 11. Practical Applications of Large Language Models in Security Scenarios