Implementing Pipelines in Azure Machine Learning
This lesson focuses on how to automate model training workflows. We look at creating reusable and shareable components that can be used in multiple pipelines, before seeing how to create pipelines using Azure Machine Learning Studio and with Python scripts. We finish with seeing how to share data between pipeline tasks.
Learning Objectives
- Creating pipeline components
- Creating and running a training pipeline
- Sharing data between pipeline steps
Intended Audience
- Students who want to learn how to train a model using Azure Machine Learning pipelines
- Students preparing for the DP-100: Designing and Implementing a Data Science Solution on Azure exam
Prerequisites
Running a Script as a Command demo commands
rm -r azure-ml-labs -f
https://github.com/MicrosoftLearn/mslearn-azure-ml.git azure-mil-labs
cd azuer-ml-labs/Labs/09
./setup.sh
pip uninstall azure-ai-ml
pip install azure-ai-ml
git clone https://github.com/MicrosoftLearn/mslearn-azure-ml.git azure-mil-labs
Hallam is a software architect with over 20 years experience across a wide range of industries. He began his software career as a Delphi/Interbase disciple but changed his allegiance to Microsoft with its deep and broad ecosystem. While Hallam has designed and crafted custom software utilizing web, mobile and desktop technologies, good quality reliable data is the key to a successful solution. The challenge of quickly turning data into useful information for digestion by humans and machines has led Hallam to specialize in database design and process automation. Showing customers how leverage new technology to change and improve their business processes is one of the key drivers keeping Hallam coming back to the keyboard.