Projects Overview
A Project is the central hub where all machine learning flows are created, stored, and executed. It serves as the foundation for developing and managing end-to-end machine learning workflows.
Setting Up a Project
Before building a flow or data pipeline, you must first create a project and select an environment to run it in. The environment provides:
Dedicated hardware for running custom projects.
Pre-installed Python packages to ensure seamless execution of recipes within the data pipeline.
Structure of a Project
Each project consists of multiple flows, which are built using key components:
Datasets – The foundation for data ingestion and processing.
Recipes – AI-assisted, rapid model, template, or API connector recipes for data transformation and analysis.
Artifacts – Outputs generated at different stages of the workflow.
Models – Machine learning models trained with the data in the project.
Charts – Visual representations of insights derived from data.
By organizing projects in this structured way, users can efficiently develop and scale their machine learning workflows.
Projects dashboard
The Projects Dashboard presents all projects within a workspace as interactive widgets, offering quick access to the project you need. Each widget provides an overview of key details, including the DataApps and schedulers created for the flow. You can also see who last modified the project, along with the date and timestamp of the most recent update.
Various sections on the Projects dashboard
This section explains various sections on the Projects dashboard page:
Project card: You can view the user who created the project, number of DataApps created for this project, total jobs scheduled for the project and the last updated time stamp.
You can view two options on the card:
Ask AI on Your Data – Click to open the Ask AI page (AI-Assisted Recipe), where you can provide a prompt to generate a recipe that transforms the dataset and produces an output in the form of a dataset, chart, model, or text.
Connect Your Data – Click to upload a dataset. This button is only visible if no dataset has been uploaded to the project.
When you hover over a project widget, an ellipsis icon appears. Clicking it reveals the following options:
Project Image – Upload an image to display on the project card.
Project Settings – Modify project details.
Copy Project – Duplicate the project within the same workspace or a different one.
Delete – Remove the project if it is no longer needed.
Search: You can search for a specific project by providing the name in the search box.
+ Project: You can create a new project. For more information, see Creating a new project.
Switch from projects list view to card view: You can use this option to switch from list view to the card view.
The card view of projects appears.
Switch from projects card view to list view: You can use this option to switch from list view to the card view.
Favorite icon: You can use this option to mark the important projects as favorites.
Favorite filter: You can use this option to narrow down the list to view only the favorite projects, particularly when managing a large number of projects.
Project-level navigation
The Canvas is the page displayed after you click on any project. You can see the following options on the canvas workspace of a project.
Project-level navigation menus - These menus are located on the left side of the screen and contain quick links to:
Canvas: This is the canvas where you can build data pipelines. To learn working on the canvas, see Canvas Overview.
Scenarios: This is where you can view all scenarios created for this project. To learn creating scenarios, see Scenarios.
Schedulers: This has the list of schedulers for this project. To set up project job runs, see Schedulers.
DataApps: This shows the dataapps created for this project. To create and run dataapps, see DataApps.
Predictions: This shows the prediction jobs created in the project. To create and manage prediction jobs, see Predictions.
Settings: This enables you to change the project settings. To change project settings, see Editing the project details.
About: This has the summary of overall project. You can generate the content for the data pipeline with AI-assistance. The significance of each entity in the data pipeline is explained by the AI. To create about content of the project, see Generating the about content for the project.
See also: To learn more about projects, read the following sections:
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