Vertex AI for AI Platform users

Vertex AI brings together AI Platform and AutoML into a single interface. This page compares Vertex AI and AI Platform, for users who are familiar with AI Platform.

Custom training

With Vertex AI, you can train models with AutoML, or you can do custom training, which is a workflow more similar to AI Platform Training.

Task	AI Platform Training	Vertex AI
Select the machine learning framework version to use	Google Cloud console users set the framework name and framework version.
	Runtime versions - When submitting a training job, specify the number of a runtime version that includes your desired framework and framework version.	Prebuilt containers - When submitting a custom training job, specify the Artifact Registry URI of a prebuilt container that corresponds to your framework and framework version.
Submit a training job using a custom container	Build your own custom container, host it on Artifact Registry, and use it to run your training app.
Set the Google Cloud region to use	Specify the name of a region when submitting a training job to a global endpoint (ml.googleapis.com).	Submit your custom training job to a regional endpoint, such as us-central1-aiplatform.googleapis.com. There is no global endpoint. Some regions that are available in AI Platform are not available in Vertex AI. See the list of supported regions on the Locations page.
Specify machine configurations for distributed training	Specify configurations named after specific roles of your training cluster (masterConfig, workerConfig, parameterServerConfig, and evaluatorConfig).	The configuration is a generic list — specify machine configurations in CustomJobSpec.workerPoolSpecs[].
Submit a training job using a Python package	Fields related to your Python package are top-level within TrainingInput.	Fields related to your Python package are organized within pythonPackageSpec.
Specify machine types	Specify any available machine type when creating a training job. Both GPUs and TPUs are supported.	Legacy machine types and scale tiers are not supported. Newer Compute Engine machine types are available. Both GPUs and TPUs are supported.
Submit a hyperparameter tuning job	Submit a training job with a hyperparameters configuration. Whether a training job is submitted with or without hyperparameter tuning, it creates a TrainingJob API resource.	Submit a hyperparameter tuning job with a studySpec configuration. This creates a top-level API resource (HyperparameterTuningJob). Custom training jobs submitted without hyperparameter tuning create a top-level CustomJob API resource.
Create a training pipeline to orchestrate training jobs with other operations	No built-in API resource for orchestration; use AI Platform Pipelines, Kubeflow, or another orchestration tool.	Create a TrainingPipeline resource to orchestrate a training job with model deployment.

Prediction

Task	AI Platform Prediction	Vertex AI
Select the machine learning framework version to use	Google Cloud console users set the framework name and framework version.
	Runtime versions - When deploying a model, specify the number of a runtime version that includes your desired framework and framework version.	Prebuilt containers - When deploying a model, specify the Artifact Registry URI of a prebuilt container that corresponds to your framework and framework version. Use the multi-regional option that matches your regional endpoint — for example, us-docker.pkg.dev for a us-central1 endpoint.
Run custom code with prediction	Use custom prediction routines.	Use custom prediction routines on Vertex AI.
Set the Google Cloud region to use	Specify the name of a region when creating a model on a global API endpoint (ml.googleapis.com).	Create your model on a regional endpoint, such as us-central1-aiplatform.googleapis.com. There is no global endpoint. Some regions that are available in AI Platform are not available in Vertex AI. See the list of supported regions on the Locations page.
Store model artifacts	Model artifacts are stored in Cloud Storage. There is no associated API resource for model artifacts.	There is managed model storage available for model artifacts, and it is associated with the Model resource. You can still deploy models stored in Cloud Storage without using a Vertex AI managed dataset.
Model deployment	You deploy a model directly to make it available for online predictions.	You create an Endpoint object, which provides resources for serving online predictions. You then deploy the model to the endpoint. To request predictions, you call the predict() method.
Request batch predictions	You can request batch predictions on models stored in Cloud Storage, and specify a runtime version in your request. Alternatively, you can request batch predictions on deployed models, and use the runtime version you specified during model deployment.	You upload your model to Vertex AI, and then you specify either a prebuilt container or a custom container to serve the predictions.
Online prediction requests	The JSON structure includes a list of instances.	The JSON structure includes a list of instances and a field for parameters.
Specify machine types	Specify any available machine type when creating a version.	Legacy online prediction machine types from AI Platform (MLS1) are not supported. Only Compute Engine machine types are available.
Deploy models	Create a model resource, and then create a version resource.	Create a model resource, create an endpoint resource, and deploy the model to the endpoint. Specify traffic splitting in the endpoint.

Vertex Explainable AI

You can get feature attributions for tabular and image models in both AI Explanations for AI Platform and Vertex Explainable AI.

Task	AI Explanations for AI Platform	Vertex Explainable AI
Get feature attributions for tabular models	Use Sampled Shapley or integrated gradients to get feature attributions for tabular models.
Get feature attributions for image models	Use integrated gradients or XRAI to get feature attributions for image models.