The LLM Engineer's Playbook: Mastering the Development of Large Language Models for Real-World Applications
Leona Lang
This audiobook is narrated by a digital voice.
The world of artificial intelligence is rapidly evolving, and at the heart of this revolution are Large Language Models (LLMs). These powerful tools are transforming how we interact with technology, offering unprecedented capabilities in natural language processing. The LLM Engineer's Playbook is an essential guide for anyone looking to navigate the complexities of developing and deploying LLMs in practical, real-world scenarios. This book provides a comprehensive roadmap for engineers, developers, and tech enthusiasts eager to harness the potential of LLMs, offering a blend of theoretical insights and hands-on techniques. Within these pages, you'll find a rich array of content designed to elevate your understanding and skills in LLM development. The book covers foundational concepts, ensuring even those new to the field can follow along, and progressively delves into more advanced topics. Key sections include the architecture and functioning of LLMs, data preparation and preprocessing, model training and fine-tuning, and best practices for deployment and maintenance. Each chapter is crafted to build on the previous one, creating a seamless learning experience. The practical examples and case studies illustrate how LLMs can be applied in various industries, from enhancing customer service chatbots to revolutionizing content creation and beyond.
Duration - 6h 34m.
Author - Leona Lang.
Narrator - Digital Voice Martin G.
Published Date - Sunday, 12 January 2025.
Copyright - © 2025 Diana Richards ©.
Location:
United States
Description:
This audiobook is narrated by a digital voice. The world of artificial intelligence is rapidly evolving, and at the heart of this revolution are Large Language Models (LLMs). These powerful tools are transforming how we interact with technology, offering unprecedented capabilities in natural language processing. The LLM Engineer's Playbook is an essential guide for anyone looking to navigate the complexities of developing and deploying LLMs in practical, real-world scenarios. This book provides a comprehensive roadmap for engineers, developers, and tech enthusiasts eager to harness the potential of LLMs, offering a blend of theoretical insights and hands-on techniques. Within these pages, you'll find a rich array of content designed to elevate your understanding and skills in LLM development. The book covers foundational concepts, ensuring even those new to the field can follow along, and progressively delves into more advanced topics. Key sections include the architecture and functioning of LLMs, data preparation and preprocessing, model training and fine-tuning, and best practices for deployment and maintenance. Each chapter is crafted to build on the previous one, creating a seamless learning experience. The practical examples and case studies illustrate how LLMs can be applied in various industries, from enhancing customer service chatbots to revolutionizing content creation and beyond. Duration - 6h 34m. Author - Leona Lang. Narrator - Digital Voice Martin G. Published Date - Sunday, 12 January 2025. Copyright - © 2025 Diana Richards ©.
Language:
English
Chapter 1: Introduction to Large Language Models 4
Duración:00:00:05
1.1 What are Large Language Models? 4
Duración:00:08:50
1.2 The Evolution of Language Models 10
Duración:00:06:49
1.3 Key Concepts and Terminology 15
Duración:00:06:09
1.4 Importance of LLMs in Modern Applications 19
Duración:00:07:22
Chapter 2: Understanding the Architecture of LLM 25
Duración:00:00:05
2.1 Transformer Architecture 25
Duración:00:06:01
2.2 Attention Mechanisms 29
Duración:00:06:46
2.3 Training and Fine-Tuning Processes 34
Duración:00:07:55
2.4 Model Scaling and Parallelism 40
Duración:00:07:46
Chapter 3: Data Preparation and Management 46
Duración:00:00:05
3.1 Data Collection Strategies 46
Duración:00:08:38
3.2 Data Cleaning and Preprocessing 52
Duración:00:06:42
3.3 Handling Large-Scale Datasets 57
Duración:00:06:15
3.4 Data Privacy and Compliance 61
Duración:00:07:55
Chapter 4: Building Your First LLM 67
Duración:00:00:04
4.1 Setting Up the Development Environment 67
Duración:00:06:37
4.2 Choosing the Right Framework 72
Duración:00:07:32
4.3 Initial Model Training 77
Duración:00:05:40
4.4 Evaluating Model Performance 81
Duración:00:06:16
Chapter 5: Advanced Training Techniques 86
Duración:00:00:04
5.1 Transfer Learning and Pretraining 86
Duración:00:07:16
5.2 Multi-task Learning 91
Duración:00:05:48
5.3 Reinforcement Learning for LLMs 96
Duración:00:07:32
5.4 Hyperparameter Tuning 101
Duración:00:06:48
Chapter 6: Optimizing Model Performance 107
Duración:00:00:04
6.1 Model Compression Techniques 107
Duración:00:06:44
6.2 Quantization and Pruning 112
Duración:00:05:33
6.3 Efficient Inference Strategies 116
Duración:00:08:09
6.4 Hardware Acceleration 121
Duración:00:08:46
Chapter 7: Ethical Considerations and Bias Mitigation 128
Duración:00:00:06
7.1 Understanding Bias in Language Models 128
Duración:00:07:10
7.2 Techniques for Bias Detection 133
Duración:00:07:35
7.3 Mitigation Strategies 138
Duración:00:07:25
7.4 Ethical Guidelines and Best Practices 143
Duración:00:08:18
Chapter 8: Deploying LLMs in Production 150
Duración:00:00:04
8.1 Deployment Architectures 150
Duración:00:06:29
8.2 Containerization and Orchestration 154
Duración:00:07:20
8.3 Scaling and Load Balancing 159
Duración:00:05:43
8.4 Monitoring and Maintenance 163
Duración:00:06:57
Chapter 9: Real-World Applications of LLMs 169
Duración:00:00:05
9.1 Natural Language Understanding 169
Duración:00:06:22
9.2 Text Generation and Summarization 173
Duración:00:07:39
9.3 Conversational Agents 179
Duración:00:07:02
9.4 Language Translation 183
Duración:00:07:18
Chapter 10: Case Studies and Success Stories 189
Duración:00:00:04
10.1 Industry Applications 189
Duración:00:10:29
10.2 Notable Implementations 196
Duración:00:10:19
10.3 Lessons Learned 203
Duración:00:08:13
10.4 Future Trends 209
Duración:00:09:07
Chapter 11: Tools and Frameworks for LLM Development 216
Duración:00:00:05
11.1 Popular Frameworks and Libraries 216
Duración:00:07:49
11.2 Development Tools and Platforms 221
Duración:00:09:35
11.3 Integrating LLMs with Other Systems 228
Duración:00:07:59
11.4 Community Resources and Support 233
Duración:00:07:39
Chapter 12: Challenges and Limitations of LLMs 240
Duración:00:00:05
12.1 Computational Constraints 240
Duración:00:05:59
12.2 Data Quality Issues 244
Duración:00:06:38
12.3 Model Interpretability 249
Duración:00:08:02
12.4 Future Research Directions 254
Duración:00:08:06
Chapter 13: The Future of Large Language Models 261
Duración:00:00:05
13.1 Emerging Trends and Innovations 261
Duración:00:09:48
13.2 The Role of LLMs in AI Evolution 268
Duración:00:10:24
13.3 Predictions and Speculations 275
Duración:00:10:06
13.4 Preparing for the Next Wave of Advancements 282
Duración:00:07:47