Migrating to Vertex AI

This page provides recommended steps and other information to help you plan and implement a migration to Vertex AI from AutoML products and AI Platform.

Vertex AI supports all features and models available in AutoML and AI Platform. However, the client libraries do not support client integration backward compatibility. In other words, you must plan to migrate your resources to benefit from Vertex AI features.

If you are planning a new project, you should build your code, job, dataset, or model with Vertex AI. This lets you take advantage of the new features and service improvements as they become available. AutoML and AI Platform remain available, but future improvements will be implemented on Vertex AI.

Use the following recommended steps to update your existing code, jobs, datasets, and models from AutoML and AI Platform to Vertex AI.

Migrating from AutoML

To update your implementation from AutoML to Vertex AI, complete these steps:

  1. Read about the major differences between AutoML and Vertex AI at Vertex AI for AutoML users.

  2. Review any potential changes in pricing (see Vertex AI migration pricing).

  3. Take inventory of your Google Cloud projects, code, jobs, datasets, models, and users with access to AutoML. Use this information to determine which resources to migrate and ensure that the correct users have access to the migrated resources.

  4. Review the changes to IAM roles, and then update service accounts and authentication for your resources.

  5. Review the list of resources that you cannot migrate and information about the migration process.

  6. Migrate your resources using either of these two methods:

  7. Read about how Vertex AI uses regional endpoints.

  8. Identify usage of AutoML APIs to help determine which of your applications use them and to identify the method calls that you want to migrate.

  9. Update your applications and workflows to use the Vertex AI API and Vertex AI features.

  10. Plan your request quota monitoring. See Quotas and limits.

Migrating from AI Platform

To update your implementation from AI Platform to Vertex AI, complete these steps:

  1. Read about the major differences between AI Platform and Vertex AI at Vertex AI for AI Platform users.

  2. Review any potential changes in pricing (see Vertex AI migration pricing).

  3. Take inventory of your Google Cloud projects, code, jobs, datasets, models, and users with access to AI Platform. Use this information to determine which resources to migrate and ensure that the correct users have access to the migrated resources.

  4. Review the changes to IAM roles, and then update service accounts and authentication for your resources.

  5. Review the list of resources that you cannot migrate and information about the migration process.

  6. Migrate your resources using either of these two methods:

  7. Read about how Vertex AI uses regional endpoints.

  8. Identify usage of AI Platform APIs to help determine which of your applications use them and to identify the method calls that you want to migrate.

  9. Update your applications and workflows to use the Vertex AI API and Vertex AI features.

  10. Plan your request quota monitoring. See Quotas and limits.

Vertex AI migration pricing

Migration is free. Resources that are created as a result of migration incur standard charges (see Vertex AI pricing). Datasets migrated from AI Platform Data Labeling Service, AutoML Vision, AutoML Video Intelligence, and AutoML Natural Language migrate to a Cloud Storage bucket, which will incur storage costs (see Cloud Storage pricing).

After migration, legacy resources are still available to use in AutoML and AI Platform. To avoid unnecessary costs, shut down or delete legacy resources after you have verified that your objects have migrated successfully.

Migration is a copy operation. After you migrate a resource, changes to the legacy resource do not affect the migrated resource.

Identifying usage of AutoML and AI Platform APIs

You can determine which of your applications use AutoML and AI Platform APIs, as well as which methods they are using. Use this information to help determine whether these API calls need to be migrated to Vertex AI.

To identify AutoML and AI Platform API calls that you might want to migrate, see the following options.

Managing changes to IAM roles and permissions

Vertex AI provides the following Identity and Access Management (IAM) roles:

  • aiplatform.admin
  • aiplatform.user
  • aiplatform.viewer
  • aiplatform.migrator

Only aiplatform.admin and aiplatform.migrator have the ability to migrate resources from AutoML and AI Platform to Vertex AI. aiplatform.user and aiplatform.viewer cannot migrate resources.

For more information on IAM roles, see Access control.

Resources that cannot be migrated

The migration tool currently cannot migrate all resources and in some cases migration is limited. Consider the following exceptions when you plan your migration.

AutoML Natural Language

  • AutoML Natural Language models are not migratable.

  • PDF text is not supported in Vertex AI, so AutoML Natural Language's PDF text is migrated as plain text generated by optical character recognition.

  • Empty datasets cannot be migrated.

  • Batch prediction jobs cannot be migrated.

AutoML Tables

  • Models created in an alpha version of AutoML Tables cannot be migrated.

  • Empty datasets cannot be migrated.

  • Batch prediction jobs cannot be migrated.

AutoML Video Intelligence

  • Models created in an alpha version of AutoML Video cannot be migrated.

  • Empty datasets cannot be migrated.

  • Batch prediction jobs cannot be migrated.

AutoML Vision

  • Models created in an alpha version of AutoML Vision cannot be migrated.

  • Empty datasets cannot be migrated.

  • Batch prediction jobs cannot be migrated.

AI Platform

  • Not all models can be migrated. Models that are migratable have these characteristics:

    • The runtime version must be 1.15 or higher.

    • The framework must be one of the following:

      • TensorFlow
      • scikit-learn
      • XGBoost
    • The Python version must be 3.7 or higher.

  • If an AI Platform model's signature-name flag has been changed from the default value, serving_default, it might migrate to Vertex AI but will not function.

  • Custom prediction routines are not migrated.

  • Jobs run on AI Platform are not migrated. You can download the metadata for your own records.

  • The Python scripts, packages, or Docker containers you run on AI Platform Training are not automatically migratable, but you can update your scripts to enable them to run on Vertex AI.

About the migration process

Before migrating your resources, review the following information first.

  • The migration tool creates a copy of your resources.

    The migration tool creates a duplicate version of your AutoML and AI Platform datasets and models on Vertex AI. Your legacy resources are not deleted. If you want, you can migrate the same resource multiple times to create several copies.

  • Migrated models are undeployed.

    For data types that support online prediction, you must create an endpoint and deploy the model to that endpoint before the model can be used to serve online predictions.

  • When an AutoML model is migrated, the migration tool automatically creates a training job at the same time.

  • Migrated datasets for some data types and objectives might not contain the same data as the current dataset.

    Datasets for certain data types are reimported from the original data source, rather than copied over from the existing dataset. If the original data source has been changed, the migrated dataset will reflect those changes. This caveat applies to the following data types and objectives:

    • AutoML Natural Language entity extraction datasets
    • AutoML Video classification and object tracking datasets
    • AutoML Vision object detection datasets
  • Migrated tabular datasets are exported as part of the migration process.

    In Vertex AI, a tabular dataset's data source is referenced rather than imported (learn more). A migrated tabular dataset is exported from the AutoML Tables dataset, stored as a CSV file in Cloud Storage or a BigQuery table in your project, and then referenced by the migrated dataset.

  • Notebooks instances do not need to be migrated.

    Notebooks instances are already available in Vertex AI.

Using the migration tool

Vertex AI provides a migration tool to help you migrate your datasets and models from AutoML and AI Platform to Vertex AI.

Steps for using the migration tool

To use the migration tool to migrate your datasets and models to Vertex AI, complete the following steps.

  1. If you haven't already enabled the Vertex AI API, on the Vertex AI Dashboard page in the Google Cloud Console, click Enable the Vertex AI API.

  2. On the Vertex AI Dashboard page in the Google Cloud Console, under Migrate to Vertex AI, click Set up migration.

  3. Under Select resources to migrate, select up to 50 assets to migrate. If you need to, you can repeat these steps to migrate more assets later.

  4. Click Next, and review the summary of the assets that you want to migrate.

  5. Click Migrate assets. Migration might take an hour or more depending on the number of assets being migrated. The migration tool sends you an email when the migration is finished.

Using the client libraries and methods to migrate resources

Use the batchMigrateResources() method and related methods to migrate your resources.

Refer to the Vertex AI API Reference documentation if you need help.

Regional endpoints

Vertex AI API endpoints are regional. For example:

us-central1-aiplatform.googleapis.com

Global endpoints are not supported for Vertex AI.

See the list of supported endpoints in the reference documentation.

Updating training scripts to run in Vertex AI

The Python scripts, packages, or Docker containers you run on AI Platform Training require the following specific changes to run on Vertex AI.

  • For jobs that write outputs to Cloud Storage, in Vertex AI, you must indicate the Cloud Storage URI for different types of outputs through environment variables. In AI Platform, the Cloud Storage URI is typically indicated with the command line argument --job-dir.

  • In Vertex AI, the TF_CONFIG variable uses the term chief to refer to the primary machine. In AI Platform, in some cases, it uses the term master.

  • When submitting a custom training job in Vertex AI, specify the Artifact Registry URI of a pre-built container that corresponds to your framework and framework version. In AI Platform, you specify a runtime version that includes the framework and framework version that you want to use.

  • Not all machine types supported by AI Platform are supported by Vertex AI.

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