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Machine Learning Engineer
1. introduction to Azure AI Studio
Microsoft Azure offers several services that developers can use to create exciting AI-based solutions. Azure AI Studio brings these services together in a single unified user interface for AI development on the Azure cloud platform.
2. getting started with prompt flow to develop language model apps in Azure AI Studio
Learn how to use prompt flow to develop applications that use language models in Azure AI Studio.
3. create a RAG-based copilot solution with your own data using Azure AI Studio
Copilots can provide you with suggestions, generate content or help you make decisions. Copilots use language models as a form of generative artificial intelligence (AI) and answer your questions based on the data they have been trained with. To ensure that a copilot retrieves information from a specific source, you can add your own data when creating a copilot with Azure AI Studio.
4. responsible generative AI in AI Studio
Amazing creative solutions are possible with generative AI, but it must be implemented responsibly to minimize the risk of generating harmful content.
Requirements
- Familiarity with basic AI concepts and services in Azure
- Experience with Python
- Describe the most important features and functions of Azure AI Studio
- Using Azure AI Studio to deploy and manage an Azure AI resource
- Using Azure AI Studio to create and manage an AI project
- Understand when to use Azure AI Studio
- Understanding the development lifecycle when creating language model applications
- Understanding what a flow is in Prompt Flow
- Explore the core components when working with the Prompt Flow
- Determine the need to ground your language model with Retrieval Augmented Generation (RAG)
- Index your data with Azure AI search so you can be searched by language models
- Create a copilot using RAG and your own data in Azure AI Studio
- Describe an overall process for the development of solutions for responsible generative AI
- Identify and prioritize potential damages that are relevant for a generative AI solution
- Measuring the presence of damage in a generative AI solution
- Minimizing damage in a generative AI solution
- Preparing to responsibly deploy and operate a generative AI solution
This course consists of training training and is led by a trainer who supervises the participants live. Theory and practice are taught with live demonstrations and practical exercises. The video conferencing software Zoom is used.
participants should be familiar with basic AI concepts and services in Azure and have experience with Python.
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