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  • Book Overview & Buying Generative AI for Cloud Solutions
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Generative AI for Cloud Solutions

Generative AI for Cloud Solutions

By : Paul Singh, Anurag Karuparti
4.9 (9)
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Generative AI for Cloud Solutions

Generative AI for Cloud Solutions

4.9 (9)
By: Paul Singh, Anurag Karuparti

Overview of this book

Generative artificial intelligence technologies and services, including ChatGPT, are transforming our work, life, and communication landscapes. To thrive in this new era, harnessing the full potential of these technologies is crucial. Generative AI for Cloud Solutions is a comprehensive guide to understanding and using Generative AI within cloud platforms. This book covers the basics of cloud computing and Generative AI/ChatGPT, addressing scaling strategies and security concerns. With its help, you’ll be able to apply responsible AI practices and other methods such as fine-tuning, RAG, autonomous agents, LLMOps, and Assistants APIs. As you progress, you’ll learn how to design and implement secure and scalable ChatGPT solutions on the cloud, while also gaining insights into the foundations of building conversational AI, such as chatbots. This process will help you customize your AI applications to suit your specific requirements. By the end of this book, you’ll have gained a solid understanding of the capabilities of Generative AI and cloud computing, empowering you to develop efficient and ethical AI solutions for a variety of applications and services.
Table of Contents (18 chapters)
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1
Part 1:Integrating Cloud Power with Language Breakthroughs
4
Part 2: Techniques for Tailoring LLMs
8
Part 3: Developing, Operationalizing, and Scaling Generative AI Applications
11
Part 4: Building Safe and Secure AI – Security and Ethical Considerations
14
Part 5: Generative AI – What’s Next?

Fine-Tuning – Building Domain-Specific LLM Applications

In developing ChatGPT-based applications, ensuring the model’s precision, relevance, and alignment to its intended purpose is paramount. As we navigate the intricacies of this technology, it becomes evident that a one-size-fits-all approach doesn’t suffice. Hence, customizing the model becomes necessary to adapt to certain specialized domains, such as medicine, biotechnology, legal, and others. This chapter delves deep into model customization for domain-specific applications via fine-tuning and parameter-efficient fine-tuning (PEFT). But how do we evaluate that our refinements truly hit the mark? How do we know that they align with human values? Through rigorous evaluation metrics and benchmarking. By understanding and applying these pivotal processes, we not only bring out the best in ChatGPT but also adhere closely to the vision of this book: generative AI for cloud solutions. We must ensure it’s...

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Generative AI for Cloud Solutions
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