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Firebase ML stores your AutoML training datasets differently, depending on
your project's pricing plan. When your project is on the Blaze pricing plan,
Firebase ML creates a new Cloud Storage bucket in your project to store
AutoML Vision Edge data. When your project is on the Spark pricing plan,
Firebase ML stores your AutoML Vision Edge data internally instead of using
your project's Cloud Storage.
If you create a dataset while on the Spark pricing plan and later upgrade to the
Blaze plan, your dataset will be available, but will still be subject to the
limitations of the Spark plan (these datasets are labeled Spark datasets in
the Firebase console). If you want your dataset to take advantage of Blaze
features, such as unlimited training examples (billed by storage use), you'll
have to migrate the Spark dataset to a new dataset.
To migrate a dataset:
Open the AutoML section of the
Firebase console. (Select your project when prompted.)
On the dataset you want to migrate, click View to open the details page,
then click Export dataset. You will download a zip file containing the
dataset's training images and labels.
Create a new dataset by uploading the zip file.
(See Train your model.)
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-15 UTC."],[],[],null,["\u003cbr /\u003e\n\nFirebase ML stores your AutoML training datasets differently, depending on\nyour project's pricing plan. When your project is on the Blaze pricing plan,\nFirebase ML creates a new Cloud Storage bucket in your project to store\nAutoML Vision Edge data. When your project is on the Spark pricing plan,\nFirebase ML stores your AutoML Vision Edge data internally instead of using\nyour project's Cloud Storage.\n| Firebase ML's AutoML Vision Edge features are deprecated. Consider using [Vertex AI](https://cloud.google.com/vertex-ai/docs/beginner/beginners-guide) to automatically train ML models, which you can either [export as TensorFlow\n| Lite models](https://cloud.google.com/vertex-ai/docs/export/export-edge-model) for on-device use or [deploy for cloud-based\n| inference](https://cloud.google.com/vertex-ai/docs/predictions/overview).\n\nIf you create a dataset while on the Spark pricing plan and later upgrade to the\nBlaze plan, your dataset will be available, but will still be subject to the\nlimitations of the Spark plan (these datasets are labeled **Spark datasets** in\nthe Firebase console). If you want your dataset to take advantage of Blaze\nfeatures, such as unlimited training examples (billed by storage use), you'll\nhave to migrate the Spark dataset to a new dataset.\n\nTo migrate a dataset:\n\n1. Open the [AutoML section](//console.firebase.google.com/project/_/ml/automl) of the\n Firebase console. (Select your project when prompted.)\n\n2. On the dataset you want to migrate, click **View** to open the details page,\n then click **Export dataset**. You will download a zip file containing the\n dataset's training images and labels.\n\n3. Create a new dataset by uploading the zip file.\n (See [Train your model](/docs/ml/train-image-labeler#train_the_model).)"]]
