Learn how to build, deploy, and manage machine learning solutions at scale using Azure Machine Learning. You'll use Python and popular ML frameworks to handle data preparation, model training, deployment, and monitoring, all within a cloud environment. Tools like MLflow and the Azure AI Foundry portal are also introduced for managing experiments and building generative AI apps.

In this course you will

• configure and manage the Azure Machine Learning workspace;
• prepare and explore data using Python for machine learning;
• optimize model training for cloud-scale perfomance;
• manage and review models within Azure ML;
• monitor and maintain machine learning solutions with Azure ML and MLflow;
• develop generative AI applications using Azure AI Foundry.

• Explore and configure the Azure Machine Learning Workspace.
• Experiment with Azure Machine Learning.
• Optimize model training with Azure Machine Learning.
• Manage and review models in Azure Machine Learning.
• Deploy and consume models with Azure Machine learning.
• Develop generative AI apps in Azure AI Foundy portal.

Instructor led live-training with exercises.

Delivered as a live, interactive training: available in-person or online, or in a hybrid format. 

Successful Azure Data Scientists start this role with a fundamental knowledge of cloud computing concepts, and experience in general data science and machine learning tools and techniques.

Specifically:

• Creating cloud resources in Microsoft Azure.
• Using Python to explore and visualize data.
• Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.
• Working with containersTo gain these prerequisite skills, take the following free online training before attending the course:
• Explore Microsoft cloud concepts.
• Create machine learning models.
• Administer containers in AzureIf you are completely new to data science and machine learning, please complete Microsoft Azure AI fundamentals first.

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.