Machine Learning Designer provides preset templates from which you can create pipelines. After you create a pipeline from a template, you can modify specific components or component settings of a pipeline to build models. This topic describes how to create a pipeline from a preset template to quickly train AI models suitable for your business scenarios.
Prerequisites
A workspace is created. For more information, see Create a workspace.
Procedure
Go to the Machine Learning Designer page.
Log on to the Machine Learning Platform for AI console.
In the left-side navigation pane, click Workspaces. On the Workspaces page, click the name of the workspace that you want to manage.
In the left-side navigation pane, choose to go to the Machine Learning Designer page.
On the Visualized Modeling (Designer) page, click the Preset Templates tab.
Machine Learning Designer provides dozens of preset templates that are developed based on different frameworks to meet requirements in a variety of sectors. The following items describe the template categories. You can choose a template based on your business scenario. For more information, see Overview.
Categories by sector: Internet, industrial, finance, education, healthcare, and scientific research and development.
Categories by algorithm type: classification, regression, and clustering. Categories by framework: TensorFlow and PyTorch.
Categories by business domain: recommendation, risk control, user growth, cross validation (CV), natural language processing (NLP), model optimization, Automatic Speech Recognition (ASR), and video.
View the templates provided, select an appropriate template, and then click Create in the template section.
In the Create Pipeline dialog box, configure the following parameters. You can use their default values.
Parameter
Description
Pipeline Name
The name of the pipeline. You can use the default name or customize a name based on your business requirements.
Pipeline Data Path
The path of the Object Storage Service (OSS) bucket that stores the temporary data and models generated when the pipeline is running. We recommend that you set this parameter.
Each time the pipeline is run, the system automatically creates a temporary directory in the following format:
<Pipeline data path>/<Task ID>/<Node ID>. This saves you from creating an OSS directory for storing data of each component and allows you to manage data in a centralized manner.Description
The description of the pipeline. Descriptions are used to identify pipelines.
Visibility
The scope at which the pipeline is visible. Valid values:
Visible to Me: The pipeline is created in the My Pipelines folder. This pipeline is visible only to you and the administrators of the current workspace.
Visible to Current Workspace: The pipeline is created in the Pipelines Visible to Workspaces folder. This pipeline is visible to all members of the current workspace.
Click OK.
It takes about 10 seconds to create the pipeline.
What to do next
After the pipeline is created, you can go to the configuration tab of the pipeline to configure the pipeline. For more information, see Build a model.