What happened to Azure Batch AI?

The Azure Batch AI service has retired. The at-scale training capabilities of Batch AI are available in Azure Machine Learning service. Migrate today.

Along with many other machine learning capabilities, the Azure Machine Learning service includes a cloud-based managed compute target for training and batch scoring machine learning models. This compute target is called Azure Machine Learning Compute and extends all the capabilities of the deprecated Batch AI service. Start migrating and using it today. You can interact with the Azure Machine Learning service through its Python SDK, command-line interface, and the Azure portal.

Support timeline

You can use your existing Azure Batch AI subscriptions for a grace period only, but the service is officially retired with no support for SLAs. No new subscription registrations are possible and no further investments or updates are being made.

The service will shut down soon with no further notice.

Migrate today.


Azure Machine Learning service is not available in Government clouds (GA is planned for June, 2019), and we will continue to support Batch AI service in that region till then.

Compare to Azure Machine Learning

It is a cloud service that you use to train, deploy, automate, and manage machine learning models, all at the broad scale that the cloud provides. Get a high-level understanding of the Azure Machine Learning service in this overview.

A typical model development lifecycle involves Data Preparation, Training & Experimentation and a Deployment phase. This end to end cycle can be orchestrated by using Machine Learning pipelines.

Flow diagram

Learn more about how the service works and its main concepts. Many of the concepts in the model training workflow are similar to existing concepts in Batch AI.

Specifically, here is a mapping of how you should think about them:

Batch AI service Azure Machine Learning service
Workspace Workspace
Cluster Compute of type AmlCompute
File servers DataStores
Experiments Experiments
Jobs Runs (allows nested runs)

Here is another view of the same table that will help you visualize things further:

Batch AI hierarchy

Flow diagram

Azure Machine Learning service hierarchy

Flow diagram

Platform capabilities

Azure Machine Learning service brings a great set of new functionalities including an end to end training->deployment stack that you can use for your AI development without having to manage any Azure resources. This table compares feature support for training between the two services.

Feature Batch AI service Azure Machine Learning service
VM size choice CPU/GPU CPU/GPU. Also supports FPGA for inferencing
AI ready Cluster (Drivers, Docker, etc.) Yes Yes
Node Prep Yes No
OS family Choice Partial No
Dedicated and LowPriority VMs Yes Yes
Auto-Scaling Yes Yes (by default)
Wait time for autoscaling No Yes
SSH Yes Yes
Cluster level Mounting Yes (FileShares, Blobs, NFS, Custom) Yes (mount or download your datastore)
Distributed Training Yes Yes
Job Execution Mode VM or Container Container
Custom Container Image Yes Yes
Any Toolkit Yes Yes (Run Python Script)
JobPreparation Yes Not yet
Job level mounting Yes (FileShares, Blobs, NFS, Custom) Yes (FileShares, Blobs)
Job Monitoring via GetJob via Run History (Richer information, Custom runtime to push more metrics)
Retrieve Job Logs and Files/Models via ListFiles and Storage APIs via Artifact service
Support for Tensorboard No Yes
VM family level quotas Yes Yes (with your previous capacity carried forward)

In addition to the preceding table, there are features in the Azure Machine Learning service that were traditionally not supported in BatchAI.

Feature Batch AI service Azure Machine Learning service
Environment Preparation No Yes (Conda Prepare and upload to ACR)
HyperParameter Tuning No Yes
Model management No Yes
Operationalization/Deployment No Via AKS and ACI
Data Preparation No Yes
Compute Targets Azure VMs Local, BatchAI (as AmlCompute), DataBricks, HDInsight
Automated Machine Learning No Yes
Pipelines No Yes
Batch Scoring Yes Yes
Portal/CLI support Yes Yes

Programming interfaces

This table presents the various programming interfaces available for each service.

Feature BatchAI service Azure Machine Learning service
SDK Java, C#, Python, Nodejs Python (both run config based and estimator based for common frameworks)
CLI Yes Not yet
Azure portal Yes Yes (except job submission)
REST API Yes Yes but distributed across microservices

Upgrading from the Preview Batch AI to the GA'ed Azure Machine Learning service gives you a better experience through concepts that are easier to use such as Estimators and Datastores. It also guarantees GA level Azure service SLAs and customer support.

Azure Machine Learning service also brings in new functionality such as automated machine learning, hyperparameter tuning, and ML pipelines, which are useful in most large-scale AI workloads. The ability to deploy a trained model without switching to a separate service helps complete the data science loop from data preparation (using the Data Prep SDK) all the way to operationalization and model monitoring.


Learn how to migrate and how the code you use maps to code in Azure Machine Learning service in the Migrate to Azure Machine Learning service article.

Get support

Batch AI has been retired and is already blocking new subscriptions from registering against the service. Reach out to us at Azure Batch AI Training Preview with any questions or if you have feedback as you migrate to Azure Machine Learning service.

Azure Machine Learning service is a generally available service. This means that it comes with a committed SLA and various support plans to choose from.

Pricing for using Azure infrastructure either through the Batch AI service or through the Azure Machine Learning service should not vary, as we only charge the price for the underlying compute in both cases. For more information, see the pricing calculator.

View the regional availability between the two services on the Azure portal.

Next steps