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.
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This course is now available as part of a multi-course, blended learning premium training bundle for a limited time! Take your Azure skills and career to the next level with multi-modal learning path bundles that lead to certification.
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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.
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 containers.
If you are completely new to data science and machine learning, please complete Microsoft Azure AI Fundamentals Training (AI-900).
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.
This course can help you prepare for the following Microsoft role-based certification exam — DP-100: Designing and Implementing a Data Science Solution on Azure
Designing and Implementing a Data Science Solution on Azure (DP-100) Delivery Methods
Microsoft Official Course content
Designing and Implementing a Data Science Solution on Azure (DP-100) Course Benefits
Use Azure to services to develop machine learning solutionsDeploy machine learning modelsAutomate Machine Learning with Azure Machine Learning serviceManage and Monitor Machine Learning Models with the Azure Machine Learning service
Azure Data Science Certification Course Outline
Module 1: Getting Started with 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.
Introduction to Azure Machine Learning
Working with Azure Machine Learning
Lab : Creating an Azure Machine Learning Workspace
After completing this module, you will be able to
Provision an Azure Machine Learning workspace
Use tools and code to work with Azure Machine Learning
Module 2: Visual Tools for Machine Learning
Module 3: Running Experiments and Training Models
Module 4: Working with Data
Module 5: Working with Compute
Module 6: Orchestrating Operations with Pipelines
Module 7: Deploying and Consuming Models
Module 8: Training Optimal Models
Module 9: Responsible Machine Learning
Module 10: Monitoring Models