Kubeflow Samples

Examples that demonstrate machine learning with Kubeflow

This section introduces the examples in the kubeflow/examples repository.

Financial time series

Last update 2019/12/20 Kubeflow v0.7

Train and serve a model for financial time series analysis using TensorFlow on GCP.

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GitHub issue summarization

Last update 2019/12/17 Kubeflow v0.3.0-rc.3

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.

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MNIST image classification

Last update 2020/02/10

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.

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Object detection - cats and dogs

Last update 2019/08/15

Train a distributed model for recognizing breeds of cats and dogs with the TensorFlow Object Detection API. Serve the model using TensorFlow.

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PyTorch MNIST

Last update 2019/07/25

Train a distributed PyTorch model on GCP and serve the model with Seldon Core.

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Ames housing value prediction

Last update 2019/02/12

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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Last update 2019/01/15 Kubeflow 0.3

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).

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Last modified 10.02.2020: Fixes a link in the samples page (#1629) (f1dd41b5)