We have introduced a low-code experience for data scientists to convert the Machine Learning (ML) development code into repeatable and reusable workflow steps of Amazon SageMaker Pipelines using an @step decorator. This sample notebook demonstrates how to build a computer vision pipeline using a combination of the @step decorator and other pipeline steps.
Specifically, this notebook builds a pipeline which:
- Uses the @step decorator to augment a retail image dataset;
- Uses the Tuning step to train and tune a model using SageMaker's Object Detection algorithm;
- Uses the Model step to create a model object for the best-performing model;
- Uses the Transform step to run the test set through the best-performing model;
- Uses the @step decorator to evaluate the results.
The screenshot below shows an example of a successful execution of this pipeline.
