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Unlocking Creativity with Azure OpenAI

Unlocking Creativity with Azure OpenAI

By : AMIT MUKHERJEE, Adithya Saladi
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Unlocking Creativity with Azure OpenAI

Unlocking Creativity with Azure OpenAI

By: AMIT MUKHERJEE, Adithya Saladi

Overview of this book

Azure OpenAI, a cutting-edge service by Microsoft, harnesses the power of OpenAI's Large Language Model (LLM) to drive cloud-based innovations within enterprises. This service integrates advanced LLM models into business functions, transforming Microsoft products like GitHub Copilot, Microsoft 365 Copilot, and Bing Chat, making them more advanced and interactive. Azure OpenAI is accessible via REST APIs, Python SDK, or Azure OpenAI Studio, opening doors to build innovative AI applications. This book is a comprehensive guide to build GenAI applications using Azure OpenAI. It begins with the fundamentals, including how to access Azure OpenAI and how to effectively utilize its REST API and Python SDK. It takes a deep dive into various AI models and emphasizes the crucial aspects of prompt engineering and fine-tuning for optimal output. It further underlines the significance of content filters and prevention of misuse, maintaining a strong focus on safety and security protocols and finally the significance of Azure OpenAI Studio in deployment and administration is emphasized. Practical applications are showcased like content generations, summarization, semantic search, code documentation and code generation. Combining Azure Cognitive services amplifies Generative AI potential and finally aligns with Microsoft's ethical AI principles.
Table of Contents (19 chapters)
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1
Part 1: Foundations of Generative AI and Azure OpenAI
5
Part 2: Practical Applications of Azure OpenAI: Real-World Use Cases
13
Part 3: Mastering Governance, Operations, and AI Optimization with Azure OpenAI

Summary

In this chapter, we significantly enhanced our movie recommender solution, adding layers of functionality that make it both more intelligent and user-friendly. We began by setting up the necessary keys and credentials, ensuring our program could securely interact with the required APIs and services. This setup is crucial because it allows our system to access powerful resources, such as OpenAI’s embedding models, which are key to understanding and processing the data.

Next, we integrated a Netflix dataset directly from Kaggle into our Jupyter notebook. By organizing this dataset into a pandas DataFrame, we created a structured environment that facilitates efficient data manipulation and analysis. This step is vital because a clean, well-organized dataset is the foundation for any data-driven solution, enabling us to focus on extracting meaningful insights.

After loading the data, we zeroed in on the titles and descriptions of the shows, recognizing that these text...

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