aihub

AI Hub ML Container Examples

This folder holds notebooks with examples of using ML Containers from the AI Hub.

The notebooks show an example workflow of:

• create a dataset
• train an ML model
• monitor the training
• validate the trained model
• deploy the trained model for serving
• get online predictions
• interactively examine the model with the What-if Tool

ML Containers

AI Hub documentation	GitHub preview	Colab Notebook
PCA	Dimensionality reduction	Dimensionality reduction
K-Means	Clustering	K-Means
Factorization Machines	Classification, Regression	Classification, Regression
Tabular Anomaly Detection	Anomaly detection	Anomaly detection
XGBoost	Classification, Regression	Classification, Regression
TF Module image	Image classification	Image classification
ResNet	Image classification	Image classification
RetinaNet	Object detection in image	Object detection in image
Tabular Data Inspection	Visualize data statistics	Visualize data statistics
KNN	Classification, Regression	Classification, Regression

Note that all of the ML containers generate an HTML report file (Run Report) that is embedded in the notebooks. GitHub renderers the notebooks in a way that doesn't show JavaScript plots from those reports.

For an example of using those ML Containers with Kubeflow pipelines, read this article.

License

By deploying or using this software you agree to comply with the AI Hub Terms of Service and the Google APIs Terms of Service. To the extent of a direct conflict of terms, the AI Hub Terms of Service will control.