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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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Free Chapter
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

AOAI Batch API

Some applications require synchronous request handling, also known as real-time inferencing, where immediate responses are necessary. However, there are numerous situations wherein responses can be deferred or rate limits may restrict the speed at which multiple queries can be processed. In such cases, batch processing jobs prove useful, particularly for tasks such as the following:

  • Large-scale data processing
  • Generating large volumes of content transforming data at scale
  • Evaluating LLM models and assess comprehensive performance

The AOAI Batch API provides a user-friendly suite of endpoints. These allow you to bundle multiple requests into a single file, initiate a batch job to process these requests asynchronously, check the batch’s status as the tasks run, and, finally, retrieve the consolidated results once processing is complete.

Compared to traditional PAUG deployments, the Batch API offers the following:

  • Cost efficiency...
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