Adding data assets to a deployment space
Adding data assets to a deployment space Learn about various ways of adding and promoting data assets to a space and data types that are used in deployments. Data can be: * A data file such as a .csv file * A connection to data that is located in a repository such as a database. * Connected data that is located in a storage bucket. For more information, see [Using data from the Cloud Object Storage service](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-space-add-assets.html?context=cdpaas&locale=encos-data). Notes: * For definitions of data-related terms, refer to [Asset types and properties](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/assets.html). You can add data to a space in one of these ways: * [Add data and connections to space by using UI](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-space-add-assets.html?context=cdpaas&locale=enadd-directly) * [Promote a data source, such as a file or a connection from an associated project](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-space-promote-assets.html) * [Save a data asset to a space programmatically](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-space-add-assets.html?context=cdpaas&locale=enadd-programmatically) * [Import a space or a project, including data assets, into an existing space](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-space-import-to-space.html). Data added to a space is managed in a similar way to data added to a Watson Studio project. For example: * Adding data to a space creates a new copy of the asset and its attachments within the space, maintaining a reference back to the project asset. If an asset such as a data connection requires access credentials, they persist and are the same whether you are accessing the data from a project or from a space. * Just like with data connection in a project, you can edit data connection details from the space. * Data assets are stored in a space in the same way that they are stored in a project. They use the same file structure for the space as the structure used for the project. Adding data and connections to space by using UI To add data or connections to space by using UI: 1. From the Assets tab of your deployment space, click Import assets. 2. Choose between adding a connected data asset, local file, or connection to a data source: * If you want to add a connected data asset, select Connected data. Choose a connection and click Import. * If you want to add a local file, select Local file > Data asset. Upload your file and click Done. * If you want to add a connection to a data source, select Data access > Connection. Choose a connection and click Import. The data asset displays in the space and is available for use as an input data source in a deployment job. Note:Some types of connections allow for using your personal platform credentials. If you add a connection or connected data that uses your personal platform credentials, tick the Use my platform login credentials checkbox. Adding data to space programmatically If you are using APIs to create, update, or delete Watson Machine Learning assets, make sure that you are using only Watson Machine Learning [API calls](https://cloud.ibm.com/apidocs/machine-learning). For an example of how to add assets programmatically, refer to this sample notebook: [Use SPSS and batch deployment with Db2 to predict customer churn](https://github.com/IBM/watson-machine-learning-samples/blob/df8e5122a521638cb37245254fe35d3a18cd3f59/cloud/notebooks/python_sdk/deployments/spss/Use%20SPSS%20and%20batch%20deployment%20with%20DB2%20to%20predict%20customer%20churn.ipynb) Data source reference types in Watson Machine Learning Data source reference types are referenced in Watson Machine Learning requests to represent input data and results locations. Use data_asset and connection_asset for these types of data sources: * Cloud Object Storage * Db2 * Database data Notes: * For Decision Optimization, the reference type is url. Example data_asset payload {"input_data_references": [{ "type": "data_asset", "connection": { }, "location": { "href": "/v2/assets/<asset_id>?space_id=<space_id>" } }] Example connection_asset payload "input_data_references": [{ "type": "connection_asset", "connection": { "id": "<connection_guid>" }, "location": { "bucket": "<bucket_name>", "file_name": "<directory_name>/<file_name>" } <other wdp-properties supported by runtimes> }] For more information, see: * Watson Machine Learning [REST API](https://cloud.ibm.com/apidocs/machine-learning) Using data from the Cloud Object Storage service Cloud Object Storage service can be used with deployment jobs through a connected data asset or a connection asset. To use data from the Cloud Object Storage service: 1. Create a connection to IBM Cloud Object Storage by adding a Connection to your project or space and selecting Cloud Object Storage (infrastructure) or Cloud Object Storage as the connector. Provide the secret key, access key, and login URL. Note:When you are creating a connection to Cloud Object Storage or Cloud Object Storage (Infrastructure), you must specify both access_key and secret_key. If access_key and secret_key are not specified, downloading the data from that connection doesn't work in a batch deployment job. For reference, see [IBM Cloud Object Storage connection](https://dataplatform.cloud.ibm.com/docs/content/wsj/manage-data/conn-cos.html) and [IBM Cloud Object Storage (infrastructure) connection](https://dataplatform.cloud.ibm.com/docs/content/wsj/manage-data/conn-cos-infra.html). 2. Add input and output files to the deployment space as connected data by using the Cloud Object Storage connection that you created. Parent topic:[Assets in deployment spaces](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-space-add-assets-all.html)