[ Web Proxy ]
URL:
Viewing: https://cloud.google.com/dataflow/docs/tutorials/python-ml-examples [Back]  [Original]
Python ML tutorials  |  Cloud Dataflow  |  Google Cloud Documentation Skip to main content
Google Cloud Documentation [Google Cloud Documentation]
Documentation
Search:
Console
[]
Start free
Overview Guides Dataflow ML Reference Samples Resources
Google Cloud Documentation [Google Cloud Documentation]
Send feedback

Python ML tutorials Stay organized with collections Save and categorize content based on your preferences.

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.

Land cover image segmentation

This land classification model uses a TensorFlow framework and satellite data from Google Earth Engine to demonstrate semantic segmentation. The tutorial uses TensorFlow in Gemini Enterprise Agent Platform to train the model, TensorFlow in Cloud Run to make real-time predictions, and Dataflow to make batch predictions. View the code on GitHub.

Open In Colab [Open In Colab]

Weather forecasting time series regression

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, Gemini Enterprise Agent Platform to train the model, Dataflow to create the dataset, and PyTorch to make local predictions. View the code on GitHub.

Open In Colab [Open In Colab]

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.

Open In Colab [Open In Colab]

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 Gemini Enterprise Agent Platform to train the model, Dataflow to create the dataset, and Gemini Enterprise Agent Platform to make predictions. View the code on GitHub.

Open In Colab [Open In Colab]

Send feedback

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2026-10-14 UTC.

Need to tell us more? [[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2026-10-14 UTC."],[],[]]
Web Proxy Viewer  |  New URL  |  Original Page