Kubeflow Samples

This section introduces the examples in thekubeflow/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).

Read

Financial time series

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

GitHub issue summarization

Infer summaries of GitHub issues from the descriptions, using a Sequence toSequence natural language processing model. You can run the tutorial in aJupyter 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 canchoose to train the model locally, using GCP, or using Amazon S3. Serve themodel using TensorFlow.

Object detection - cats and dogs

PyTorch MNIST

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.