This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

• Develop generative AI apps in Azure AI Foundry portal

• Explore Azure Machine Learning workspace resources and assets
• Explore developer tools for workspace interaction
• Make data available in Azure Machine Learning
• Work with compute targets in Azure Machine Learning
• Work with environments in Azure Machine Learning

• Find the best classification model with Automated Machine Learning
• Track model training in Jupyter notebooks with MLflow

• Run a training script as a command job in Azure Machine Learning
• Track model training with MLflow in jobs
• Perform hyperparameter tuning with Azure Machine Learning
• Run pipelines in Azure Machine Learning

• Register an MLflow model in Azure Machine Learning
• Create and explore the Responsible AI dashboard for a model in Azure Machine Learning

• Deploy a model to a managed online endpoint
• Deploy a model to a batch endpoint

• Plan and prepare to develop AI solutions on Azure
• Choose and deploy models from the model catalog in Azure AI Foundry portal
• Develop an AI app with the Azure AI Foundry SDK
• Get started with prompt flow to develop language model apps in the Azure AI Foundry
• Develop a RAG-based solution with your own data using Azure AI Foundry
• Fine-tune a language model with Azure AI Foundry
• Implement a responsible generative AI solution in Azure AI Foundry
• Evaluate generative AI performance in Azure AI Foundry portal