sanpil / pipeline-demo

AML Pipeline Demo

Azure Machine Learning Pipeline

Overview

The Azure Machine Learning Pipelines enables data scientists to create and manage multiple simple and complex workflows concurrently. A typical pipeline would have multiple tasks to prepare data, train, deploy and evaluate models. Individual steps in the pipeline can make use of diverse compute options (for example: CPU for data preparation and GPU for training) and languages.

Learn more about how to create your first machine learning pipeline.

Why build pipelines?

With pipelines, you can optimize your workflow with simplicity, speed, portability, and reuse. When building pipelines with Azure Machine Learning, you can focus on what you know best — machine learning — rather than infrastructure.

Notebooks

Our official Notebook repo is https://aka.ms/aml-pipeline-notebooks. This repo is just for the LearnAI webinar. In the official repro, you will find more extensive documentation.

• The first type of notebooks will introduce you to core Azure Machine Learning Pipelines features. These notebooks below belong in this category, and are designed to go in sequence; they're all located in the "intro-to-pipelines" folder.

• The second type of notebooks illustrate more sophisticated scenarios, and are independent of each other. These notebooks include:

1. pipeline-batch-scoring.ipynb
2. pipeline-style-transfer.ipynb