Building Your First Data Science Project in Microsoft Azure
Behind every machine learning model, there is a great service solving issues like versioning, shareability, deployment, and reproducibility. This course will teach you how to properly start with the right foot on your Data Science projects.
What you'll learn
Creating a machine learning model in Azure may seem daunting at first. There are a ton of configurations and specifications to take into account. In this course, Building Your First Data Science Project in Microsoft Azure, you’ll learn how to properly start with the right foot on your Data Science projects with Azure Machine Learning. First, you’ll explore what Azure Machine Learning is. Next, you’ll discover how to select and configure your development environment to ensure success. Finally, you’ll learn how to configure deployments and all the deployment targets within Azure. When you’re finished with this course, you’ll have the skills and knowledge of Azure Machine Learning needed to start your Ml projects the right way.
Table of contents
- A Walkthrough of Model Development 5m
- What Is an ML Workspace? 2m
- Demo: Create an ML Workspace 5m
- A Preview on Development Environments 5m
- Demo: Setting up the Local Environment Part1 5m
- Demo: Setting up the Local Environment Part2 6m
- Demo: Configuring a DSVM Environment 4m
- Demo: Using Compute Instances 6m
- Key Takeaways and Tips 2m
About the author
Axel Sirota is a Microsoft Certified Trainer with a deep interest in Deep Learning and Machine Learning Operations. He has a Masters degree in Mathematics and after researching in Probability, Statistics and Machine Learning optimisation, he works as an AI and Cloud Consultant as well as being an Author and Instructor at Pluralsight, Develop Intelligence, and O'Reilly Media.