Building Large Language Model(LLM) Applications
À propos de cet e-book
Beginning with an introduction to LLMs and their importance in modern applications, the book explores the history, key concepts, and popular architectures like GPT and BERT. Readers learn how to set up their development environment, including hardware and software requirements, installing necessary tools and libraries, and leveraging cloud services for efficient development and deployment.
Data preparation is essential for training LLMs, and the book provides insights into gathering and cleaning data, annotating and labeling data, and handling imbalanced data to ensure high-quality training datasets. Training large language models involves understanding training basics, best practices, distributed training techniques, and fine-tuning pre-trained models for specific tasks.
Developing LLM applications requires designing user interfaces, integrating LLMs into existing systems, and building interactive features such as chatbots, text generation, sentiment analysis, named entity recognition, and machine translation. Advanced LLM techniques like prompt engineering, transfer learning, multi-task learning, and zero-shot learning are explored to enhance model capabilities.
Deployment and scalability strategies are discussed to ensure smooth deployment of LLM applications while managing costs effectively. Security and ethics in LLM apps are addressed, covering bias detection, fairness, privacy, security, and ethical considerations to build responsible AI solutions.
Real-world case studies illustrate the practical applications of LLMs in various domains, including customer service, healthcare, and finance. Troubleshooting and optimization techniques help readers address common issues and optimize model performance.
Looking towards the future, the book highlights emerging trends and developments in LLM technology, emphasizing the importance of staying updated with advancements and adhering to ethical AI practices. "Building LLM Apps" serves as a comprehensive resource for developers, data scientists, and business professionals seeking to harness the power of large language models in their applications.
À propos de l'auteur
I am Anand V, a seasoned Enterprise Architect with extensive experience in AI and Generative AI technologies. My expertise includes implementing advanced AI solutions such as H20, Google TensorFlow, and MNIST, and leading digital transformation projects incorporating AI/ML, AR/VR, and RPA. I have integrated Generative AI tools, such as OpenAI's GPT, into enterprise architectures to enhance customer experiences and drive innovation. My work includes developing transformer models, fine-tuning pre-trained language models, and implementing neural network architectures for natural language processing (NLP) tasks. Additionally, I have utilized techniques such as deep reinforcement learning, variational autoencoders, and GANs for complex data synthesis and predictive analytics. My leadership in deploying AI-driven methodologies has significantly improved business performance across various industries.
