Blog post: https://aws.amazon.com/blogs/machine-learning/introducing-amazon-sagemaker-components-for-kubeflow-pipelines/
Video with demo walkthrough: https://www.youtube.com/watch?v=LKmkiUdhV58
Runs an Amazon SageMaker hyperparameter tuning job to optimize the following hyperparameters:
- learning-rate: [0.0001, 0.1] log scale
- optimizer : [sgd, adam]
- batch-size: [32, 128, 256]
- model-type: [resnet, custom model]
Input: N/A
Output: best hyperparameters
During hyperparameter search in the previous step, models are only trained for 10 epochs to determine well performing hyperparameters. In the current step the best hyperparameters are taken and the epochs are updated to 80 to give the best hyperparameters an opportunity to deliver higher accuracy in the next step.
Input: best hyperparameters
Output: best hyperparameters with updated epochs (80)
Run an Amazon SageMaker training job using the best hyperparameters and for higher epochs.
Input: best hyperparameters with updated epochs (80)
Output: training job name
Create an Amazon SageMaker model artifact
Input: training job name
Output: model artifact name
Deploy a model with Amazon SageMaker deployment
Input: model artifact name
Output: N/A
