Harness the power of AI with innovative, real-world applications, and unprecedented productivity boosts, powered by the latest advancements in AI technology like ChatGPT and OpenAI
Purchase of the print or Kindle book includes a free PDF eBook
Key Features
- Explore the theory behind generative AI models and the road to GPT3 and GPT4
- Become familiar with ChatGPT's applications to boost everyday productivity
- Learn to embed OpenAI models into applications using lightweight frameworks like LangChain
Book Description
Generative AI models and AI language models are becoming increasingly popular due to their unparalleled capabilities. This book will provide you with insights into the inner workings of the LLMs and guide you through creating your own language models. You'll start with an introduction to the field of generative AI, helping you understand how these models are trained to generate new data.
Next, you'll explore use cases where ChatGPT can boost productivity and enhance creativity. You'll learn how to get the best from your ChatGPT interactions by improving your prompt design and leveraging zero, one, and few-shots learning capabilities. The use cases are divided into clusters of marketers, researchers, and developers, which will help you apply what you learn in this book to your own challenges faster.
You'll also discover enterprise-level scenarios that leverage OpenAI models' APIs available on Azure infrastructure; both generative models like GPT-3 and embedding models like Ada. For each scenario, you'll find an end-to-end implementation with Python, using Streamlit as the frontend and the LangChain SDK to facilitate models' integration into your applications.
By the end of this book, you'll be well equipped to use the generative AI field and start using ChatGPT and OpenAI models' APIs in your own projects.
What you will learn
- Understand generative AI concepts from basic to intermediate level
- Focus on the GPT architecture for generative AI models
- Maximize ChatGPT's value with an effective prompt design
- Explore applications and use cases of ChatGPT
- Use OpenAI models and features via API calls
- Build and deploy generative AI systems with Python
- Leverage Azure infrastructure for enterprise-level use cases
- Ensure responsible AI and ethics in generative AI systems
Who this book is for
This book is for individuals interested in boosting their daily productivity; businesspersons looking to dive deeper into real-world applications to empower their organizations; data scientists and developers trying to identify ways to boost ML models and code; marketers and researchers seeking to leverage use cases in their domain – all by using Chat GPT and OpenAI Models.
A basic understanding of Python is required; however, the book provides theoretical descriptions alongside sections with code so that the reader can learn the concrete use case application without running the scripts.
Table of Contents
- Introduction to Generative AI
- OpenAI and ChatGPT – Beyond the Market Hype
- Getting Familiar with ChatGPT
- Understanding Prompt Design
- Boosting Day-to-Day Productivity with ChatGPT
- Developing the Future with ChatGPT
- Mastering Marketing with ChatGPT
- Research Reinvented with ChatGPT
- OpenAI and ChatGPT for Enterprises – Introducing Azure OpenAI
- Trending Use Cases for Enterprises
- Epilogue and Final Thoughts
Valentina Alto graduated in 2021 in data science. Since 2020, she has been working at Microsoft as an Azure solution specialist, and since 2022, she has been focusing on data and AI workloads within the manufacturing and pharmaceutical industry. She has been working closely with system integrators on customer projects to deploy cloud architecture with a focus on modern data platforms, data mesh frameworks, IoT and real-time analytics, Azure Machine Learning, Azure Cognitive Services (including Azure OpenAI Service), and Power BI for dashboarding. Since commencing her academic journey, she has been writing tech articles on statistics, machine learning, deep learning, and AI in various publications and has authored a book on the fundamentals of machine learning with Python.
