Working on a project

Use this procedure to build ML flow within this project. The hardware configured will run the flow in this project.

1. Click on a specific project in which you want to create a data pipeline. By default, you land on the Canvas page to build the data pipeline using the template, AI-assisted, code, or Rapid-model recipes.

2. Click the plus icon and select Dataset, else click +New Dataset on the canvas workspace to upload the dataset. The later option can be viewed only in a project with no flows.

Once uploaded, you can see the dataset icon on the canvas.

1. Click the dataset icon to open the side panel to select the recipe type you want to run on the database to perform data transformations.

Add Dataset Flow

1. Click the plus icon and select the recipe type from the following options:

   • Template

   • AI-assisted

   • Rapid Model

   • Code

2. Select Template to add the recipes to the data pipeline and perform data transformations from the available list of predefined standard templates.

Here, we are explaining by selecting the template recipe type. This takes you to the template recipe page.

1. Click Transformations on the Template recipe (standard recipe) page.

2. Select the transform based on the transformation you want to perform on the dataset. For example, select EDA Data Profiler.

EDA Profiler Transform

1. Click Add to add this standard template into the pipeline. Once you add, you can see the recipe icon on the canvas.

2. Click Run to run this recipe in the pipeline and generate the output, which can be a dataset, chart or both.

End to End Flow