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COURSE DETAILS
Course Code: AI-3016; Duration: 1 Day; Instructor-led
WHAT YOU WILL LEARN
Generative Artificial Intelligence (AI) is becoming more accessible through easy-to-use platforms like Azure AI Studio. Learn how to build generative AI applications like custom copilots that use language models and prompt flow to provide value to your users.
AUDIENCE
- Intermediate
- Data Scientist
- AI Engineer
- Azure AI services
PREREQUISITES
Before starting this module, you should be familiar with fundamental AI concepts and services in Azure. Consider completing the Get started with artificial intelligence learning path first.
METHODOLOGY
This program will be conducted with interactive lectures, PowerPoint presentations, discussions and practical exercises
OUTLINES
Module 1: Introduction to Azure AI Studio
In this module, you explore the way in which Microsoft Azure offers multiple services that enable developers to build amazing AI-powered solutions. Azure AI Studio brings these services together in a single unified experience for AI development on the Azure cloud platform.
Learning objectives
By the end of this module, you’ll be able to:
- Describe core features and capabilities of Azure AI Studio
- Use Azure AI Studio to provision and manage an Azure AI resource
- Use Azure AI Studio to create and manage an AI project
- Understand when to use Azure AI Studio
Overview
- Introduction
- What is Azure AI Studio?
- How does Azure AI Studio work
- When to use Azure AI Studio
- Exercise – Explore Azure AI Studio
- Knowledge check
- Summary
Module 2: Explore and deploy models from the model catalog in Azure AI Studio
Explore the various language models that are available through the Azure AI Studio’s model catalog. Understand how to select, deploy, and test a model, and to improve its performance.
Learning objectives
By the end of this module, you’ll be able to:
- Select a language model from the model catalog.
- Deploy a model to an endpoint.
- Test a model and improve the performance of the model.
Overview
- Introduction
- Explore the language models in the model catalog
- Deploy a model to an endpoint
- Improve the performance of a language model
- Exercise – Explore, deploy, and chat with language models
- Knowledge check
- Summary
Module 3: Get started with prompt flow to develop language model apps in the Azure AI Studio
Learn about how to use prompt flow to develop applications that leverage language models in the Azure AI Studio.
Learning objectives
By the end of this module, you’ll be able to:
- Understand the development lifecycle when creating language model applications.
- Understand what a flow is in prompt flow.
- Explore the core components when working with prompt flow.
Overview
- Introduction
- Understand the development lifecycle of a large language model (LLM) app
- Understand core components and explore flow types
- Explore connections and runtimes
- Explore variants and monitoring options
- Exercise – Get started with prompt flow
- Knowledge check
- Summary
Module 4: Build a RAG-based copilot solution with your own data using Azure AI Studio
Copilots can work alongside you to provide suggestions, generate content, or help you make decisions. Copilots use language models as a form of generative artificial intelligence (AI) and will answer your questions using the data they were trained on. To ensure a copilot retrieves information from a specific source, you can add your own data when building a copilot with the Azure AI Studio.
Learning objectives
By the end of this module, you’ll be able to:
- Identify the need to ground your language model with Retrieval Augmented Generation (RAG)
- Index your data with Azure AI Search to make it searchable for language models
- Build a copilot using RAG on your own data in the Azure AI Studio
Overview
- Introduction
- Understand how to ground your language model
- Make your data searchable
- Build a copilot with prompt flow
- Exercise – Create a custom copilot that uses your own data
- Knowledge check
- Summary
Module 5: Integrate a fine-tuned language model with your copilot in the Azure AI Studio
Train a base language model on a chat-completion task. The model catalog in the Azure AI Studio offers many open-source models that can be fine-tuned for your specific model behavior needs.
Learning objectives
By the end of this module, you’ll be able to:
- Understand when to fine-tune a model.
- Prepare your data to fine-tune a chat completion model.
- Fine-tune a base model in the Azure AI Studio.
Overview
- Introduction
- Understand when to fine-tune a language model
- Prepare your data to fine-tune a chat completion model
- Explore fine-tuning language models in Azure AI Studio
- Exercise – Fine-tune a foundation model
- Knowledge check
- Summary
Module 6: Evaluate the performance of your custom copilot in the Azure AI Studio
Evaluating copilots is essential to ensure your custom copilots meet user needs, provide accurate responses, and continuously improve over time. Discover how to assess and optimize the performance of your custom copilot using the tools and features available in the Azure AI Studio.
Learning objectives
By the end of this module, you’ll be able to:
- Understand model benchmarks.
- Perform manual evaluations.
- Assess your copilot with AI-assisted metrics.
- Configure evaluation flows in the Azure AI Studio.
Overview
- Introduction
- Assess the model performance
- Manually evaluate the performance of a model
- Assess the performance of your custom copilot
- Exercise – Evaluate the performance of your custom copilot
- Knowledge check
- Summary
Module 7: Responsible generative AI in AI Studio
Generative AI enables amazing creative solutions, but must be implemented responsibly to minimize the risk of harmful content generation.
Learning objectives
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
Overview
- Introduction
- Plan a responsible generative AI solution
- Identify potential harms
- Measure potential harms
- Mitigate potential harms
- Operate a responsible generative AI solution
- Exercise – Explore content filters in Azure AI Studio
- Knowledge check
- Summary
