Creating deployment spaces
Creating deployment spaces Create a deployment space to store your assets, deploy assets, and manage your deployments. Required permissions: All users in your IBM Cloud account with the Editor IAM platform access role for all IAM enabled services or for Cloud Pak for Data can manage to create deployment spaces. For more information, see [IAM Platform access roles](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/roles.htmlplatform). A deployment space is not associated with a project. You can publish assets from multiple projects to a space. For example, you might have a test space for evaluating deployments, and a production space for deployments you want to deploy in business applications. Follow these steps to create a deployment space: 1. From the navigation menu, select Deployments > New deployment space. Enter a name for your deployment space. 2. Optional: Add a description and tags. 3. Select a storage service to store your space assets. * If you have a [Cloud Object Storage](https://dataplatform.cloud.ibm.com/docs/content/wsj/admin/storage-options.html) repository that is associated with your IBM Cloud account, choose a repository from the list to store your space assets. * If you do not have a [Cloud Object Storage](https://dataplatform.cloud.ibm.com/docs/content/wsj/admin/storage-options.html) repository that is associated with your IBM Cloud account, you are prompted to create one. 4. Optional: If you want to deploy assets from your space, select a machine learning service instance to associate with your deployment space. To associate a machine learning instance to a space, you must: * Be a space administrator. * Have admin access to the machine learning service instance that you want to associate with the space. For more information, see [Creating a Watson Machine Learning service instance](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-service-instance.html). Tip: If you want to evaluate assets in the space, switch to the Manage tab and associate a Watson OpenScale instance. 5. Optional: Assign the space to a deployment stage. Deployment stages are used for [MLOps](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/modelops-overview.html), to manage access for assets in various stages of the AI lifecycle. They are also used in [governance](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/xgov-overview.html), for tracking assets. Choose from: * Development for assets under development. Assets that are tracked for governance are displayed in the Develop stage of their associated use case. * Testing for assets that are being validated. Assets that are tracked for governance are displayed in the Validate stage of their associated use case. * Production for assets in production. Assets that are tracked for governance are displayed in the Operate stage of their associated use case. 6. Optional: Upload space assets, such as [exported project](https://dataplatform.cloud.ibm.com/docs/content/wsj/manage-data/export-project.html) or [exported space](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-space-export.html). If the imported space is encrypted, you must enter the password. Tip: If you get an import error, clear your browser cookies and then try again. 7. Click Create. Viewing and managing deployment spaces * To view all deployment spaces that you can access, click Deployments on the navigation menu. * To view any of the details about the space after you create it, such as the associated service instance or storage ID, open your deployment space and then click the Manage tab. * Your space assets are stored in a Cloud Object Storage repository. You can access this repository from IBM Cloud. To find the bucket ID, open your deployment space, and click the Manage tab. Learn more To learn more about adding assets to a space and managing them, see [Assets in deployment spaces](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-space-add-assets-all.html). To learn more about creating a space and accessing its details programmatically, see [Notebook on managing spaces](https://dataplatform.cloud.ibm.com/exchange/public/entry/view/e5e78be14e2260ccb4bcf8181d0967e3). To learn more about handling spaces programmatically, see [Python client](https://ibm.github.io/watson-machine-learning-sdk/) or [REST API](https://cloud.ibm.com/apidocs/machine-learning). Parent topic:[Deployment spaces](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-spaces_local.html)