- Course
- Cloud
Overview of Azure ML Studio and SDK
Explore Azure ML Studio and SDK. This course will teach you to navigate the UI, run experiments, and use the SDK to manage ML workflows in the cloud with confidence.
What you'll learn
You’ve built machine learning models locally, but when it comes to production-grade experimentation and collaboration, you need a scalable cloud platform like Azure ML. In this course, Overview of Azure ML Studio and SDK, you’ll learn to perform common ML platform tasks using both the Azure ML Studio interface and the Python SDK. First, you’ll explore how to navigate Azure ML Studio and identify key components like datasets, jobs, and pipelines. Next, you’ll discover how to launch notebooks, attach compute, and run simple ML workflows entirely in Azure ML Studio. Finally, you’ll understand how to use the Azure ML SDK to programmatically submit jobs, and learn to assess when to use the SDK, CLI, or Studio. When you’re finished with this course, you’ll have the skills and knowledge needed to use Azure ML Studio and SDK to build, run, and track machine learning experiments in the cloud.
Table of contents
About the author
Deepak Goyal is a technology expert and instructor with deep experience in cloud, data engineering, AI, architects, and leadership.