Develop custom copilots with Azure AI Studio

Generative künstliche Intelligenz (KI) wird durch einfach zu verwendende Plattformen wie Azure KI Studio immer zugänglicher. Hier erfahren Sie, wie Sie generative KI-Anwendungen wie benutzerdefinierte Copilots erstellen, die Sprachmodelle und Prompt flow verwenden, um Ihren Benutzern einen Mehrwert zu bieten.

• Intermediate
• Data Scientist
• AI Engineer
• Azure AI services

Bevor Sie mit diesem Modul beginnen, sollten Sie sich mit grundlegenden KI-Konzepten und -Diensten in Azure vertraut machen. 

Introduction to Azure AI Foundry

• What is Azure AI Foundry?
• How does Azure AI Foundry work
• When to use Azure AI Foundry

Explore and deploy models from the model catalog in Azure AI Foundry portal

• Explore the language models in the model catalog
• Deploy a model to an endpoint
• Improve the performance of a language model

Get started with prompt flow to develop language model apps in the Azure AI Foundry

• 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

Build a RAG-based agent with your own data using Azure AI Foundry

• Understand how to ground your language model
• Make your data searchable
• Build an agent with prompt flow

Fine-tune a language model with Azure AI Foundry

• 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

Evaluate the performance of generative AI apps with Azure AI Foundry

• Assess the model performance
• Manually evaluate the performance of a model
• Assess the performance of your generative AI apps

Responsible generative AI

• Plan a responsible generative AI solution
• Identify potential harms
• Measure potential harms
• Mitigate potential harms
• Operate a responsible generative AI solution