[DSC MENA 24] Hany_Saad_Gheit_-_Azure_OpenAI_service.pptx

Supercharge your software development with Azure OpenAI Service! Azure cloud platform provides access to cutting-edge AI models for diverse tasks. Explore different models for generating content, translating languages, and even generating code. Leverage data grounding to fine-tune models for your specific needs. Discover how Azure OpenAI Service accelerates innovation and injects intelligence into your software creations.

1. Unleash AI Power in Your Software: Exploring Azure OpenAI Service

2. Hany Saad Technology professional with a rich background in software engineering and development field. Currently working as a Custom Development ( .Net ) TSP at ITWorx . Worked as lecturer and training manager at the Information Technology Institute (ITI), Ministry of Communications and Information Technology, where he contributes significantly to ICT capacity building in Egypt. Worked in other different positions with different organizations through more than 18 years. https://www.linkedin.com/in/hanysaad TSP (Technology solution professional) @ITWorx

3. Microsoft Azure 3 Microsoft Azure is a comprehensive cloud platform by Microsoft offering services for computing, storage, and networking, enabling users to build, deploy, and manage applications through global data centers.

4. Azure AI services 4

5. Azure OpenAI Service Azure OpenAI Service is a cloud-based platform provided by Microsoft that integrates OpenAI's advanced AI models, including GPT (Generative Pre-trained Transformer), into Azure's cloud services. This service allows developers and businesses to incorporate powerful natural language processing, generation, and understanding capabilities into their applications, leveraging the scalability and reliability of Azure's infrastructure.

6. Azure openai service - Models Azure OpenAI Service models Models Description GPT-4 A set of models include GPT-4 and GPT-4 Turbo Preview GPT-3.5 A set of models that improve on GPT-3 and can understand and generate natural language and code. Embeddings A set of models that can convert text into numerical vector form to facilitate text similarity. DALL-E (Preview) A series of models in preview that can generate original images from natural language. Whisper (Preview) A series of models in preview that can transcribe and translate speech to text. Text to speech (Preview) A series of models in preview that can synthesize text to speech.

7. Azure openai service – Fine tuning models Fine Tuning: Is a machine learning process where a pre-trained model is further trained or adjusted on a new, typically smaller dataset, to specialize or improve its performance on specific tasks. This process leverages the knowledge the model has gained during its initial training, making it more effective and efficient for particular applications than training a model from scratch. Customize a model with fine-tuning Azure OpenAI Service lets you tailor our models to your personal datasets by using a process known as fine-tuning . The following models support fine-tuning: gpt-35-turbo-0613 gpt-35-turbo-1106 babbage-002 davinci-002

8. Comparing Azure OpenAI and OpenAI Azure OpenAI Service gives customers advanced language AI with OpenAI GPT-4, GPT-3, Codex, DALL-E, Whisper, and text to speech models with the security and enterprise promise of Azure. Azure OpenAI co-develops the APIs with OpenAI , ensuring compatibility and a smooth transition from one to the other. With Azure OpenAI , customers get the security capabilities of Microsoft Azure while running the same models as OpenAI . Azure OpenAI offers private networking, regional availability, and responsible AI content filtering.

9. Azure openai service – use cases

10. Azure Openai service -Key concepts Prompt: is a text input given to an AI model to elicit a specific response or output, guiding the model's generation process or decision-making. Completion: is the output or response generated by an AI model based on a given prompt, representing the model's attempt to complete, answer, or extend the input it received. Tokens: For text tokens Azure OpenAI processes text by breaking it down into tokens. Tokens can be words or just chunks of characters. For example, the word “hamburger” gets broken up into the tokens “ham”, “bur” and “ger”, while a short and common word like “pear” is a single token. Many tokens start with a whitespace, for example “ hello” and “ bye”. Image tokens: The token cost of an input image depends on two main factors: the size of the image and the detail setting (low or high) used for each image. Resources: Azure OpenAI is a new product offering on Azure. You can get started with Azure OpenAI the same way as any other Azure product where you create a resource , or instance of the service, in your Azure Subscription. Deployments: Once you create an Azure OpenAI Resource, you must deploy a model before you can start making API calls and generating text. Endpoint: I s a URL used to access OpenAI models on Azure, enabling applications to send requests and receive AI-generated responses via API calls. RAG: RAG, or Retrieval-Augmented Generation, is a methodology in natural language processing that combines the retrieval of relevant documents or data with a generative model to enhance the generation of text.

11. Azure Openai service – Getting started Prerequisites An Azure subscription Access to Azure OpenAI in the selected Azure subscription. Currently, you must submit an application to access Azure OpenAI Service. To apply for access, complete this form Create a resource Configure network security Deploy a model Sign in to Azure OpenAI Studio Choose the subscription and the Azure OpenAI resource to work with Select Create new deployment and configure the needed fields Resources: Create and deploy an Azure OpenAI Service resource

12. Azure Openai service – Making calls You can start making calls to your deployed model via: From The Language Studio Language Studio is a set of UI-based tools that lets you explore, build, and integrate features from Azure AI Language into your applications. Getting started with Language Studio Using SDKs Getting started with the Azure AI SDK Azure OpenAI client library for .NET Using REST API Azure OpenAI Service REST API

13. Azure Openai service – Making calls (REST API) Create a completion POST https://{your-resource-name}.openai.azure.com/openai/deployments/{deployment-id}/completions?api-version={api-version} Parameter Type Required? Default Description prompt string or array Optional <\|endoftext\|> The prompt or prompts to generate completions for, encoded as a string, or array of strings. <\|endoftext\|> is the document separator that the model sees during training, so if a prompt isn't specified the model generates as if from the beginning of a new document. max_tokens integer Optional 16 The maximum number of tokens to generate in the completion. The token count of your prompt plus max_tokens can't exceed the model's context length. Most models have a context length of 2048 tokens (except for the newest models, which support 4096). temperature number Optional 1 What sampling temperature to use, between 0 and 2. Higher values mean the model takes more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer. We generally recommend altering this or top_p but not both. role string Yes N/A Indicates who is giving the current message. Can be system,user,assistant,tool , or function.

14. Microsoft Semantic Kernel 14 Semantic Kernel is an open-source SDK that lets you easily build agents that can call your existing code. As a highly extensible SDK, you can use Semantic Kernel with models from OpenAI , Azure OpenAI , Hugging Face, and more! By combining your existing C#, Python, and Java code with these models, you can build agents that answer questions and automate processes. It integrates Large Language Models (LLMs) like OpenAI , Azure OpenAI , and Hugging Face with conventional programming languages like C#, Python, and Java. Semantic Kernel achieves this by allowing you to define plugins that can be chained together in just a few lines of code. What makes Semantic Kernel special, however, is its ability to automatically orchestrate plugins with AI. With Semantic Kernel planners, you can ask an LLM to generate a plan that achieves a user's unique goal. Afterwards, Semantic Kernel will execute the plan for the user.

15. Q&A