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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

Azure OpenAI Scaling

AOAI typically imposes constraints on the volume of calls permitted. With Azure OpenAI, these limitations manifest as token limits (TPM, Tokens Per Minute) and restrictions on the number of requests per minute (RPM). Nevertheless, these quotas are confined to individual subscriptions, regions, and specific models. As a result, numerous customers opt for multiple Azure OpenAI (AOAI) resources across various regions to achieve maximum throughput. Although this configuration in a “PAUG” setup does not address latency issues, the subsequent section will delve into resolving latency problems using PTU.

In PAUG when the capacity limits are reached, The AOAI returns a 429 or TooManyRequests HTTP status code, along with a Retry-After response header specifying the duration of second you should wait before attempting the next request. Handling these errors is typically managed on the client-side by SDKs, which is effective when dealing with a single API...

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