Google Cloud AI Platform Operators

Google Cloud AI Platform (formerly known as ML Engine) can be used to train machine learning models at scale, host trained models in the cloud, and use models to make predictions for new data. AI Platform is a collection of tools for training, evaluating, and tuning machine learning models. AI Platform can also be used to deploy a trained model, make predictions, and manage various model versions.

The legacy versions of AI Platform Training, AI Platform Prediction, AI Platform Pipelines, and AI Platform Data Labeling Service are deprecated and will no longer be available on Google Cloud after their shutdown date. All the functionality of legacy AI Platform and new features are available on the Vertex AI platform.

Prerequisite tasks

To use these operators, you must do a few things:

Launching a Job

This function is deprecated. All the functionality of legacy MLEngine and new features are available on the Vertex AI platform.

Creating a model

A model is a container that can hold multiple model versions. A new model can be created through the MLEngineCreateModelOperator. The model field should be defined with a dictionary containing the information about the model. name is a required field in this dictionary.

Warning

This operator is deprecated. The model is created as a result of running Vertex AI operators that create training jobs of any types. For example, you can use CreateCustomPythonPackageTrainingJobOperator. The result of running this operator will be ready-to-use model saved in Model Registry.

tests/system/google/cloud/ml_engine/example_mlengine.py[source]

create_custom_python_package_training_job = CreateCustomPythonPackageTrainingJobOperator(
    task_id="create_custom_python_package_training_job",
    staging_bucket=f"gs://{CUSTOM_PYTHON_GCS_BUCKET_NAME}",
    display_name=PACKAGE_DISPLAY_NAME,
    python_package_gcs_uri=PYTHON_PACKAGE_GCS_URI,
    python_module_name=PYTHON_MODULE_NAME,
    container_uri=TRAIN_IMAGE,
    model_serving_container_image_uri=DEPLOY_IMAGE,
    bigquery_destination=f"bq://{PROJECT_ID}",
    # run params
    dataset_id=tabular_dataset_id,
    model_display_name=MODEL_DISPLAY_NAME,
    replica_count=REPLICA_COUNT,
    machine_type=MACHINE_TYPE,
    accelerator_type=ACCELERATOR_TYPE,
    accelerator_count=ACCELERATOR_COUNT,
    training_fraction_split=TRAINING_FRACTION_SPLIT,
    validation_fraction_split=VALIDATION_FRACTION_SPLIT,
    test_fraction_split=TEST_FRACTION_SPLIT,
    region=REGION,
    project_id=PROJECT_ID,
)

Getting a model

This function is deprecated. All the functionality of legacy MLEngine and new features are available on the Vertex AI platform.

Creating model versions

This function is deprecated. All the functionality of legacy MLEngine and new features are available on the Vertex AI platform. For model versioning please check: Model versioning with Vertex AI

Managing model versions

This function is deprecated. All the functionality of legacy MLEngine and new features are available on the Vertex AI platform. For model versioning please check: Model versioning with Vertex AI

Making predictions

This function is deprecated. All the functionality of legacy MLEngine and new features are available on the Vertex AI platform.

Cleaning up

This function is deprecated. All the functionality of legacy MLEngine and new features are available on the Vertex AI platform.

Evaluating a model

This function is deprecated. All the functionality of legacy MLEngine and new features are available on the Vertex AI platform. To create and view Model Evaluation, please check the documentation: Evaluate models using Vertex AI

Reference

For further information, look at:

Was this entry helpful?