Learning Outcomes ML Studio
Learning Outcomes
1
Understand Azure ML Studio's position within the Microsoft Azure ecosystem
2
How to navigate the ML Studio interface and understand its core components
3
Create and manage compute resources for machine learning workloads
4
Find the best machine learning algorithm for your data with Automated ML
5
Train and store PyTorch models using both Jupyter notebooks and Azure ML jobs
6
Implement data labelling workflows for supervised learning projects
Important Note
Whilst machine learning choices will be explained throughout, this resource is not an in-depth guide and may require some additional learning outside to understand fully. This resource is to help give you the tools and understanding to properly use Machine Learning Studio in your everyday workflows.
If you want to learn more about Machine Learning, click below for the link to our course.