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Saiba como treinar modelos de machine learning para classificação e previsão seguindo as etapas
nos notebooks interativos. Estes tutoriais integram o Dataflow a
fluxos de trabalho de machine learning completos. Também é possível conferir os tutoriais no
GitHub.
Este modelo de previsão do tempo usa um framework PyTorch
e dados de satélite do Google Earth Engine
para prever a precipitação nas próximas duas e seis horas.
O tutorial usa o PyTorch para criar uma rede totalmente convolucional,
a Vertex AI para treinar o modelo,
o Dataflow para criar o conjunto de dados e o PyTorch para fazer previsões locais.
Confira o código no GitHub.
Classificação da série temporal do Global Fishing Watch
Esse modelo de classificação usa um framework do TensorFlow
e dados de localização do Maritime Mobile Service Identity (MMSI)
para classificar se um navio está pescando por hora.
O tutorial usa o Keras e o TensorFlow para treinar o modelo,
o Dataflow para criar o conjunto de dados
e o Keras no Cloud Run para fazer previsões locais.
Confira o código no GitHub.
Classificação de imagens de animais selvagens
Esse modelo de classificação usa uma estrutura do AutoML para
criar um modelo treinado para reconhecer espécies de animais nas fotos de armadilhas com câmeras.
O tutorial usa o AutoML na Vertex AI
para treinar o modelo, o Dataflow para criar o conjunto de dados e a
Vertex AI para fazer previsões.
Confira o código no GitHub.
[[["Fácil de entender","easyToUnderstand","thumb-up"],["Meu problema foi resolvido","solvedMyProblem","thumb-up"],["Outro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Informações incorretas ou exemplo de código","incorrectInformationOrSampleCode","thumb-down"],["Não contém as informações/amostras de que eu preciso","missingTheInformationSamplesINeed","thumb-down"],["Problema na tradução","translationIssue","thumb-down"],["Outro","otherDown","thumb-down"]],["Última atualização 2025-09-04 UTC."],[[["\u003cp\u003eThese interactive notebooks provide tutorials on training machine learning models for classification and prediction, integrating Dataflow into end-to-end workflows.\u003c/p\u003e\n"],["\u003cp\u003eThe land cover image segmentation tutorial uses TensorFlow and Google Earth Engine data to perform semantic segmentation, with Vertex AI for training, Cloud Run for real-time predictions, and Dataflow for batch predictions.\u003c/p\u003e\n"],["\u003cp\u003eThe weather forecasting tutorial utilizes PyTorch and satellite data to forecast precipitation, employing Vertex AI for training, Dataflow for dataset creation, and PyTorch for local predictions.\u003c/p\u003e\n"],["\u003cp\u003eThe global fishing watch tutorial employs TensorFlow and MMSI location data to classify ships as fishing or not, using Dataflow to create the dataset and Cloud Run to make predictions.\u003c/p\u003e\n"],["\u003cp\u003eThe wildlife image classification tutorial utilizes AutoML within Vertex AI to recognize animal species in camera trap photos, with Dataflow used to create the dataset and Vertex AI for predictions.\u003c/p\u003e\n"]]],[],null,["# Python ML tutorials\n\nLearn how to train machine learning models for classification and prediction by following the steps in\ninteractive notebooks. These tutorials integrate Dataflow into\nend-to-end machine learning workflows. You can also view the tutorials in\n[GitHub](https://github.com/GoogleCloudPlatform/python-docs-samples/tree/main/people-and-planet-ai).\n\n*** ** * ** ***\n\nLand cover image segmentation\n-----------------------------\n\nThis land classification model uses a [TensorFlow](https://www.tensorflow.org/)\nframework and satellite data from\n[Google Earth Engine](https://earthengine.google.com/) to demonstrate semantic segmentation.\nThe tutorial uses [TensorFlow in Vertex AI](/vertex-ai/docs/start/tensorflow)\nto train the model, TensorFlow in [Cloud Run](/run/docs) to\nmake real-time predictions, and Dataflow to make batch predictions.\n[View the code on GitHub.](https://github.com/GoogleCloudPlatform/python-docs-samples/tree/main/people-and-planet-ai/land-cover-classification)\n\n[](https://colab.sandbox.google.com/github/GoogleCloudPlatform/python-docs-samples/blob/main/people-and-planet-ai/land-cover-classification/README.ipynb)\n\n*** ** * ** ***\n\nWeather forecasting time series regression\n------------------------------------------\n\nThis weather forecasting model uses a [PyTorch](/vertex-ai/docs/start/pytorch)\nframework and satellite data from\n[Google Earth Engine](https://earthengine.google.com/) to\nforecast precipitation for the next two and six hours.\nThe tutorial uses PyTorch to create a fully convolutional network,\n[Vertex AI](/vertex-ai/docs/start/introduction-unified-platform) to train the\nmodel, Dataflow to create the dataset, and PyTorch to make local predictions.\n[View the code on GitHub.](https://github.com/GoogleCloudPlatform/python-docs-samples/tree/main/people-and-planet-ai/weather-forecasting)\n\n[](https://colab.sandbox.google.com/github/GoogleCloudPlatform/python-docs-samples/blob/main/people-and-planet-ai/weather-forecasting/notebooks/1-overview.ipynb)\n\n*** ** * ** ***\n\nGlobal fishing watch time series classification\n-----------------------------------------------\n\nThis classification model uses a [TensorFlow](https://www.tensorflow.org/)\nframework and Maritime Mobile Service Identity\n(MMSI) location data to classify whether a ship is fishing every hour.\nThe tutorial uses [Keras](https://keras.io/) and TensorFlow to train the\nmodel, Dataflow to create the dataset, and Keras in\n[Cloud Run](/run/docs) to make local predictions.\n[View the code on GitHub.](https://github.com/GoogleCloudPlatform/python-docs-samples/tree/main/people-and-planet-ai/timeseries-classification)\n\n[](https://colab.sandbox.google.com/github/GoogleCloudPlatform/python-docs-samples/blob/main/people-and-planet-ai/timeseries-classification/README.ipynb)\n\n*** ** * ** ***\n\nWildlife image classification\n-----------------------------\n\nThis classification model uses an [AutoML](/automl/docs) framework to\ncreate a model trained to recognize animal species from camera trap pictures.\nThe tutorial uses [AutoML in Vertex AI](/vertex-ai/docs/beginner/beginners-guide)\nto train the model, Dataflow to create the dataset, and\n[Vertex AI](/vertex-ai/docs/start/introduction-unified-platform) to make predictions.\n[View the code on GitHub.](https://github.com/GoogleCloudPlatform/python-docs-samples/tree/main/people-and-planet-ai/image-classification)\n\n[](https://colab.sandbox.google.com/github/GoogleCloudPlatform/python-docs-samples/blob/main/people-and-planet-ai/image-classification/README.ipynb)"]]
