This section introduces the examples in the kubeflow/examples repo.
Semantic code search
Use a Sequence to Sequence natural language processing model to perform a semantic code search. This tutorial runs in a Jupyter notebook and uses Google Cloud Platform (GCP).
Financial time series
Train and serve a model for financial time series analysis using TensorFlow on GCP.
GitHub issue summarization
Infer summaries of GitHub issues from the descriptions, using a Sequence to Sequence natural language processing model. You can run the tutorial in a Jupyter notebook or using TFJob. You use Seldon Core to serve the model.
MNIST image classification
Train and serve an image classification model using the MNIST dataset. You can choose to train the model locally, using GCP, or using Amazon S3. Serve the model using TensorFlow.
Object detection - cats and dogs
Train a distributed model for recognizing breeds of cats and dogs with the TensorFlow Object Detection API. Serve the model using TensorFlow.
Train a distributed PyTorch model on GCP and serve the model with Seldon Core.
Ames housing value prediction
Train an XGBoost model using the Kaggle Ames Housing Prices prediction on GCP. Use Seldon Core to serve the model locally, or GCP to serve it in the cloud.
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