# Creating batch deployments in Watson Machine Learning #

A batch deployment processes input data from a file, data connection, or connected data in a storage bucket, and writes the output to a selected destination\.

## Before you begin ##

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1.  Save a model to a deployment space\.
2.  Promote or add the input file for the batch deployment to the space\. For more information, see [Deployment spaces](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-spaces_local.html)\.

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## Supported frameworks ##

Batch deployment is supported for these frameworks and asset types:

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 *  Decision Optimization
 *  PMML
 *  Python functions
 *  PyTorch\-Onnx
 *  Tensorflow
 *  Scikit\-learn
 *  Python scripts
 *  Spark MLlib
 *  SPSS
 *  XGBoost

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**Notes:**

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 *  You can create batch deployments only of Python functions and models based on the PMML framework programmatically\.
 *  Your list of deployment jobs can contain two types of jobs: `WML deployment job` and `WML batch deployment`\.
 *  When you create a batch deployment (through the UI or programmatically), an extra `default` deployment job is created of the type `WML deployment job`\. The extra job is a parent job that stores all deployment runs generated for that batch deployment that were triggered by the Watson Machine Learning API\.
 *  The standard `WML batch deployment` type job is created only when you create a deployment from the UI\. You cannot create a `WML batch deployment` type job by using the API\.
 *  The limitations of `WML deployment job` are as follows:
    
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     *  The job cannot be edited.
     *  The job cannot be deleted unless the associated batch deployment is deleted.
     *  The job doesn't allow scheduling.
     *  The job doesn't allow notifications.
     *  The job doesn't allow changing retention settings.
    
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For more information, see [Data sources for scoring batch deployments](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/deploy-batch-data-sources.html)\. For more information, see [Batch deployment input details by framework](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/deploy-batch-input-by-framework.html)

## Creating a batch deployment ##

To create a batch deployment:

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1.  From the deployment space, click the name of the saved model that you want to deploy\. The model detail page opens\.
2.  Click **New deployment**\.
3.  Choose **Batch** as the deployment type\.
4.  Enter a name and an optional description for your deployment\.
5.  Select a [hardware specification](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-hardware-configs.html)\.
6.  Click **Create**\. When status changes to **Deployed**, your deployment is created\.

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Note: Additionally, you can create a batch deployment by using any of these interfaces:

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 *  Watson Studio user interface, from an Analytics deployment space
 *  Watson Machine Learning Python Client
 *  Watson Machine Learning REST APIs

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## Creating batch deployments programmatically ##

See [Machine learning samples and examples](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-samples-overview.html) for sample notebooks that demonstrate creating batch deployments that use the Watson Machine Learning [REST API](https://cloud.ibm.com/apidocs/machine-learning) and Watson Machine Learning [Python client library](https://ibm.github.io/watson-machine-learning-sdk/)\.

## Viewing deployment details ##

Click the name of a deployment to view the details\.

![View deployment details](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/images/batch-details.png)

You can view the configuration details such as hardware and software specifications\. You can also get the deployment ID, which you can use in API calls from an endpoint\. For more information, see [Looking up a deployment endpoint](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/deploy-online.html)\.

## Learn more ##

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 *  For more information, see [Creating jobs in deployment spaces](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/create-jobs.html)\.
 *  Refer to [Machine Learning samples and examples](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-samples-overview.html) for links to sample notebooks that demonstrate creating batch deployments that use the Watson Machine Learning [REST API](https://cloud.ibm.com/apidocs/machine-learning-cp) and Watson Machine Learning [Python client library](https://ibm.github.io/watson-machine-learning-sdk/)\.

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**Parent topic:**[Managing predictive deployments](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-deploy-general.html)

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