1. Explore and configure the Azure Machine Learning workspace
Throughout this learning path you explore and configure the Azure Machine Learning workspace. Learn how you can create a workspace and what you can do with it. Explore the various developer tools you can use to interact with the workspace. Configure the workspace for machine learning workloads by creating data assets and compute resources.
2. Experiment with Azure Machine Learning
Learn how to find the best model with automated machine learning (AutoML) and by experimenting in notebooks.
3. Optimize model training with Azure Machine Learning
Learn how to optimize model training in Azure Machine Learning by using scripts, jobs, components and pipelines.
4. Manage and review models in Azure Machine Learning
Learn how to manage and review models in Azure Machine Learning by using MLflow to store your model files and using responsible AI features to evaluate your models.
5. Deploy and consume models with Azure Machine Learning
Learn how to deploy a model to an endpoint. When you deploy a model, you can get real-time or batch predictions by calling the endpoint.
6.Develop generative AI apps in Azure
Generative Artificial Intelligence (AI) is becoming more accessible through comprehensive development platforms like Microsoft Foundry. Learn how to build generative AI applications that use language models to chat with your users.