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
Duration:00:00:05
1.1 What are Large Language Models? 4
Duration:00:08:50
1.2 The Evolution of Language Models 10
Duration:00:06:49
1.3 Key Concepts and Terminology 15
Duration:00:06:09
1.4 Importance of LLMs in Modern Applications 19
Duration:00:07:22
Chapter 2: Understanding the Architecture of LLM 25
Duration:00:00:05
2.1 Transformer Architecture 25
Duration:00:06:01
2.2 Attention Mechanisms 29
Duration:00:06:46
2.3 Training and Fine-Tuning Processes 34
Duration:00:07:55
2.4 Model Scaling and Parallelism 40
Duration:00:07:46
Chapter 3: Data Preparation and Management 46
Duration:00:00:05
3.1 Data Collection Strategies 46
Duration:00:08:38
3.2 Data Cleaning and Preprocessing 52
Duration:00:06:42
3.3 Handling Large-Scale Datasets 57
Duration:00:06:15
3.4 Data Privacy and Compliance 61
Duration:00:07:55
Chapter 4: Building Your First LLM 67
Duration:00:00:04
4.1 Setting Up the Development Environment 67
Duration:00:06:37
4.2 Choosing the Right Framework 72
Duration:00:07:32
4.3 Initial Model Training 77
Duration:00:05:40
4.4 Evaluating Model Performance 81
Duration:00:06:16
Chapter 5: Advanced Training Techniques 86
Duration:00:00:04
5.1 Transfer Learning and Pretraining 86
Duration:00:07:16
5.2 Multi-task Learning 91
Duration:00:05:48
5.3 Reinforcement Learning for LLMs 96
Duration:00:07:32
5.4 Hyperparameter Tuning 101
Duration:00:06:48
Chapter 6: Optimizing Model Performance 107
Duration:00:00:04
6.1 Model Compression Techniques 107
Duration:00:06:44
6.2 Quantization and Pruning 112
Duration:00:05:33
6.3 Efficient Inference Strategies 116
Duration:00:08:09
6.4 Hardware Acceleration 121
Duration:00:08:46
Chapter 7: Ethical Considerations and Bias Mitigation 128
Duration:00:00:06
7.1 Understanding Bias in Language Models 128
Duration:00:07:10
7.2 Techniques for Bias Detection 133
Duration:00:07:35
7.3 Mitigation Strategies 138
Duration:00:07:25
7.4 Ethical Guidelines and Best Practices 143
Duration:00:08:18
Chapter 8: Deploying LLMs in Production 150
Duration:00:00:04
8.1 Deployment Architectures 150
Duration:00:06:29
8.2 Containerization and Orchestration 154
Duration:00:07:20
8.3 Scaling and Load Balancing 159
Duration:00:05:43
8.4 Monitoring and Maintenance 163
Duration:00:06:57
Chapter 9: Real-World Applications of LLMs 169
Duration:00:00:05
9.1 Natural Language Understanding 169
Duration:00:06:22
9.2 Text Generation and Summarization 173
Duration:00:07:39
9.3 Conversational Agents 179
Duration:00:07:02
9.4 Language Translation 183
Duration:00:07:18
Chapter 10: Case Studies and Success Stories 189
Duration:00:00:04
10.1 Industry Applications 189
Duration:00:10:29
10.2 Notable Implementations 196
Duration:00:10:19
10.3 Lessons Learned 203
Duration:00:08:13
10.4 Future Trends 209
Duration:00:09:07
Chapter 11: Tools and Frameworks for LLM Development 216
Duration:00:00:05
11.1 Popular Frameworks and Libraries 216
Duration:00:07:49
11.2 Development Tools and Platforms 221
Duration:00:09:35
11.3 Integrating LLMs with Other Systems 228
Duration:00:07:59
11.4 Community Resources and Support 233
Duration:00:07:39
Chapter 12: Challenges and Limitations of LLMs 240
Duration:00:00:05
12.1 Computational Constraints 240
Duration:00:05:59
12.2 Data Quality Issues 244
Duration:00:06:38
12.3 Model Interpretability 249
Duration:00:08:02
12.4 Future Research Directions 254
Duration:00:08:06
Chapter 13: The Future of Large Language Models 261
Duration:00:00:05
13.1 Emerging Trends and Innovations 261
Duration:00:09:48
13.2 The Role of LLMs in AI Evolution 268
Duration:00:10:24
13.3 Predictions and Speculations 275
Duration:00:10:06
13.4 Preparing for the Next Wave of Advancements 282
Duration:00:07:47