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- Microsoft Azure offers multiple services that enable developers to build amazing AI-powered solutions. Proper planning and preparation involves identifying the services you'll use and creating an optimal working environment for your development team.
By the end of this module, you'll be able to:
- Identify common AI capabilities that you can implement in applications
- Describe Microsoft Foundry and considerations for using it
- Describe Foundry Tools and considerations for using them
- Identify appropriate developer tools and SDKs for an AI project
- Describe considerations for responsible AI
- Explore how to select appropriate models from the model catalog using benchmarks, deploy them to endpoints, and evaluate their performance using manual and automated approaches in Microsoft Foundry portal.
By the end of this module, you'll be able to:
- Explore and filter models in the model catalog
- Compare models using benchmark metrics for quality, safety, cost, and performance
- Deploy a model to an endpoint and test it in the playground
- Evaluate model performance using manual and automated approaches
- Understand different evaluation metrics and when to use them
- Use Microsoft Foundry to develop generative AI chat applications with projects and the Responses API.
After completing this module, you'll be able to:
- Describe the process for creating a generative AI chat application with Microsoft Foundry.
- Use the Chat playground to explore models and generate code samples.
- Choose an endpoint, authentication method, and client SDK for your app development.
- Use the Responses API to generate AI responses in applications.
- Use the ChatCompletions API to generate AI responses in applications.
- Tools enable models to perform tasks and interact with external systems, enabling them to extend their capabilities beyond basic chat interactions.
After completing this module, you'll be able to:
- Describe the capabilities of generative AI tools.
- Use the code_interpreter tool to run code and analyze data.
- Use the web_search tool to retrieve real-time information from the internet.
- Use the file_search tool to access and analyze files.
- Use the function tool to run custom code.
- Explore complementary strategies to optimize generative AI model performance, including prompt engineering, system messages, model parameters, Retrieval Augmented Generation (RAG), and fine-tuning. Learn when to use each strategy and how to combine them.
By the end of this module, you'll be able to:
- Apply prompt engineering techniques including system messages, few-shot learning, and model parameters to optimize model output.
- Understand when and how to ground a language model using Retrieval Augmented Generation (RAG).
- Identify when fine-tuning a model improves behavioral consistency.
- Compare optimization strategies and determine when to combine them.
- A practical approach to implementing generative AI responsibly, and exploring guardrails in AI Foundry portal.
By the end of this module, you'll be able to:
- Describe an overall process for responsible generative AI solution development
- Identify and prioritize potential harms relevant to a generative AI solution
- Measure the presence of harms in a generative AI solution
- Mitigate harms in a generative AI solution
- Prepare to deploy and operate a generative AI solution responsibly
