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Learn how to train machine learning models for classification and prediction by following the steps in
interactive notebooks. These tutorials integrate Dataflow into
end-to-end machine learning workflows. You can also view the tutorials in
GitHub.
This weather forecasting model uses a PyTorch
framework and satellite data from
Google Earth Engine to
forecast precipitation for the next two and six hours.
The tutorial uses PyTorch to create a fully convolutional network,
Vertex AI to train the
model, Dataflow to create the dataset, and PyTorch to make local predictions.
View the code on GitHub.
Global fishing watch time series classification
This classification model uses a TensorFlow
framework and Maritime Mobile Service Identity
(MMSI) location data to classify whether a ship is fishing every hour.
The tutorial uses Keras and TensorFlow to train the
model, Dataflow to create the dataset, and Keras in
Cloud Run to make local predictions.
View the code on GitHub.
Wildlife image classification
This classification model uses an AutoML framework to
create a model trained to recognize animal species from camera trap pictures.
The tutorial uses AutoML in Vertex AI
to train the model, Dataflow to create the dataset, and
Vertex AI to make predictions.
View the code on GitHub.
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