Microsoft Azure AI Engineer: Developing ML Pipelines in Microsoft Azure
This course is for data pros, developers, and IT pros with different areas of responsibility who need to collaborate effectively on data science projects and iteratively develop repeatable, high-quality machine learning models in Microsoft Azure.
What you'll learn
At the core of being a Microsoft Azure AI engineer rests the need for effective collaboration. In this course, Microsoft Azure AI Engineer: Developing ML Pipelines in Microsoft Azure, you will learn how to develop, deploy, and monitor repeatable, high-quality machine learning models with the Microsoft Azure Machine Learning service. First, you will understand how to create no-code machine learning pipelines using the Azure ML service visual designer. Next, you will explore how to train ML models using Python, Jupyter notebooks, and the Microsoft Azure Machine Learning workspace. Finally, you will discover how to monitor your Azure Machine Learning environments from the perspective of the data scientist and data engineer. When you are finished with this course, you will have a foundational knowledge of the Microsoft Azure Machine Learning service that will help you as you move forward in the Microsoft Azure AI engineer job role.
Table of contents
- Overview 1m
- Understand the Azure Machine Learning Workspace 2m
- Create a Workspace 2m
- Demo: Deploy an Azure ML Workspace 5m
- Demo: Getting Familiar with the Workspace 3m
- Demo: Working with Jupyter Notebooks 4m
- Role-Based Access Control (RBAC) in Azure 2m
- Demo: Secure Azure ML with RBAC 2m
- Demo: Create and Test a Custom RBAC Role 6m
- Summary 2m
- Overview 1m
- Monitoring the ML Model Training Process 1m
- Demo: Monitoring Experiments in Azure Machine Learning Studio 2m
- Demo: Using the Azure ML Pyhon SDK to Monitor Model Training 3m
- Monitoring the Azure Machine Learning Service 1m
- Demo: Plotting Azure ML Resource Metrics and Raising Alerts 4m
- Demo: Querying Azure ML Log Data 3m
- Summary 2m