About this course
In this course you will learn how to use Azure Machine Learning to operate machine learning workloads in the cloud. This course teaches you to:
- Build on your existing data science and machine learning knowledge.
- Leverage cloud services to perform machine learning at scale.
- Explore considerations for responsible machine learning.
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.
After completing the course, students will be able to:
- Create an Azure Machine Learning workspace, and manage compute, data, and coding environments for machine learning workloads.
- Use Visual tools in Azure Machine Learning for machine learning model training and deployment.
- Create and run experiments that log metrics and train machine learning models.
- Create and manage datastores and datasets, and use data in machine learning experiments.
- Create and manage compute resources, and use them to run machine learning experiments at scale in the cloud.
- Use Pipelines to orchestrate machine learning operations.
- Deploy predictive models as real-time or batch inference services, and consume them from client applications.
- Find the optimal model for your data by using hyperparameter tuning and automated machine learning.
- Apply principles and techniques that support responsible machine learning practices.
- Monitor usage and data drift for deployed models.
This course is directly mapped to and supports learning for the DP-100 Designing and Implementing a Data Science Solution on Azure certification exam (Microsoft Certified: Azure Data Scientist Associate). To pass the certification test, studying outside the course may be required to ensure all the concepts are fully understood.
Successful Azure Data Scientists start this course 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 AI-900T00: Microsoft Azure AI Fundamentals first.