Designing and Implementing a Data Science Solution on Azure
AUDIENCE PROFILE
This course is aimed at data scientists and those with significant responsibilities in training and deploying machine learning models.
PREREQUSITES
Before attending this course, students must have:
- Azure Fundamentals
- Understanding of data science including how to prepare data, train models, and evaluate competing models to select the best one.
- How to program in the Python programming language and use the Python libraries: pandas, scikit-learn, matplotlib, and seaborn.
About the course
COURSE OUTLINE
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
Register for Training
Schedule
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