DP-100: Designing and Implementing a Data Science Solution on Azure

Deploy and consume models with Azure Machine Learning

• Register an MLflow model in Azure Machine Learning
• Manage and compare models in Azure Machine Learning

Plan and prepare to develop AI solutions on Azure

• Describe Azure AI Services and considerations for using them
• Describe Azure AI Foundry and considerations for using it

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

• Select a language model from the model catalog
• Deploy a model to an endpoint
• Test a model and improve the performance of the model

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

• 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

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

• 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 an agent using RAG on your own data in the Azure AI Foundry portal

Fine-tune a language model with Azure AI Foundry

• Prepare your data to fine-tune a chat completion model
• Fine-tune a base model in the Azure AI Foundry portal

Evaluate the performance of generative AI apps with Azure AI Foundry

• Understand model benchmarks
• Perform manual evaluations
• Assess your generative AI apps with AI-assisted metrics
• Configure evaluation flows in the Azure AI Foundry portal

Responsible generative AI

• 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