Designing and Implementing a Data Science Solution on Azure

• Introduction to the Azure Machine Learning SDK
• Use Automated Machine Learning in Azure Machine Learning
• Create a classification model with Azure Machine Learning designer
• Train a machine learning model with Azure Machine Learning
• Work with Data in Azure Machine Learning
• Work with Compute in Azure Machine Learning
• Orchestrate machine learning with pipelines
• Deploy real-time machine learning services with Azure Machine Learning
• Deploy batch inference pipelines with Azure Machine Learning
• Tune hyperparameters with Azure Machine Learning
• Automate machine learning model selection with Azure Machine Learning
• Explore differential privacy
• Explain machine learning models with Azure Machine Learning
• Detect and mitigate unfairness in models with Azure Machine Learning
• Monitor data drift with Azure Machine Learning
• Monitor models with Azure Machine Learning

Module 1: Doing Data Science on Azure

The student will learn about the data science process and the role of the data scientist. This is then applied to understand how Azure services can support and augment the data science process.

Lessons

• Introduce the Data Science Process
• Overview of Azure Data Science Options
• Introduce Azure Notebooks

Module 2: Doing Data Science with Azure Machine Learning service

The student will learn how to use Azure Machine Learning service to automate the data science process end to end.

Lessons

• Introduce Azure Machine Learning (AML) service
• Register and deploy ML models with AML service

Module 3: Automate Machine Learning with Azure Machine Learning service

In this module, the student will learn about the machine learning pipeline and how the Azure Machine Learning service’s AutoML and HyperDrive can automate some of the laborious parts of it.

Lessons

• Automate Machine Learning Model Selection
• Automate Hyperparameter Tuning with HyperDrive

Module 4: Manage and Monitor Machine Learning Models with the Azure Machine Learning service

In this module, the student will learn how to automatically manage and monitor machine learning models in the Azure Machine Learning service.

Lessons

• Manage and Monitor Machine Learning Models

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.