# Managing predictive deployments #

For proper deployment, you must set up a deployment space and then select and configure a specific deployment type\. After you deploy assets, you can manage and update them to make sure they perform well and to monitor their accuracy\.

To be able to deploy assets from a space, you must have a machine learning service instance that is provisioned and associated with that space\. For more information, see [Associating a service instance with a space](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-service-instance.html#associating-instance-with-space)\.

Online and batch deployments provide simple ways to create an online scoring endpoint or do batch scoring with your models\.

If you want to implement a custom logic:

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 *  Create a Python function to use for creating your online endpoint
 *  Write a notebook or script for batch scoring

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Note: If you create a notebook or a script to perform batch scoring such an asset runs as a platform job, not as a batch deployment\.

### Deployable assets ###

Following is the list of assets that you can deploy from a Watson Machine Learning space, with information on applicable deployment types:

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List of assets that you can deploy

| Asset type | Batch deployment | Online deployment |
| ---------- | ---------------- | ----------------- |
| Functions  | Yes              | Yes               |
| Models     | Yes              | Yes               |
| Scripts    | Yes              | No                |

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An R Shiny app is the only asset type that is supported for web app deployments\.

**Notes:**  

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 *  A deployment job is a way of running a batch deployment, or a self\-contained asset like a flow in Watson Machine Learning\. You can select the input and output for your job and choose to run it manually or on a schedule\. For more information, see [Creating a deployment job](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/create-jobs.html)\.
 *  Notebooks and flows use notebook environments\. You can run them in a deployment space, but they are not deployable\.

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For more information, see:

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 *  [Creating online deployments](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/deploy-online.html)
 *  [Creating batch deployments](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/deploy-batch-details.html)
 *  [Deploying Python functions](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-deploy-py-function.html)
 *  [Deploying scripts](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/deploy-script.html)

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After you deploy assets, you can manage and update them to make sure they perform well and to monitor their accuracy\. Some ways to manage or update a deployment are as follows:

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 *  [Manage deployment jobs](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/deploy-jobs.html)\. After you create one or more jobs, you can view and manage them from the **Jobs** tab of your deployment space\.
 *  [Update a deployment](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-deploy-update.html)\. For example, you can replace a model with a better\-performing version without having to create a new deployment\.
 *  [Scale a deployment](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-deploy-scaling.html) to increase availability and throughput by creating replicas of the deployment\.
 *  [Delete a deployment](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-deploy-delete.html) to remove a deployment and free up resources\.

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## Learn more ##

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 *  [Full list of asset types that can be added to a deployment space](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-space-add-assets-all.html)

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**Parent topic:**[Deploying and managing models](https://dataplatform.cloud.ibm.com/docs/content/wsj/wmls/wmls-deploy-overview.html)

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