Instructor-led (classroom)
Intermediate
3 days
English
Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.
Before attending this course, students must have:
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.
A 360-degree learning approach that you can adapt to your learning style
Engage and learn more with these live and highly-interactive classes alongside your peers
24/7 Teaching Assistance Keep engaged with integrated teaching
Projects provide you with sample work to show prospective employers.
Real-world projects relevant to what youāre learning throughout the program
A support team focused on helping you succeed alongside a peer community
Introduction to Azure Machine Learning
In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.
Lessons
Ā
Lab : Creating an Azure Machine Learning Workspace
Lab : Working with Azure Machine Learning Tools
After completing this module, you will be able to
No-Code Machine Learning with Designer
This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume.
Lessons
Ā
Lab : Creating a Training Pipeline with the Azure ML Designer
Lab : Deploying a Service with the Azure ML Designer
After completing this module, you will be able to
Running Experiments and Training Models
In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models.
Lessons
Ā
Lab : Running Experiments
Lab : Training and Registering Models
Designing and Implementing a Data Science Solution on Azure
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