Control Center

1. How do I access Control Center?

For every installation, you will be provided with a URL for the Control Center along with admin credentials to further create roles and users within that installation. Once you click on the URL, you will be directed to a login page, where you can now login using your credentials.

2. What are the different user roles and the permissions associated with them?

Administrators can create, modify, and delete roles. Each role can be mapped to a set of product features (available in the specific instance) that the role can access. Four predefined roles are available in any new instance: “Admin”, “SU”, “PM”, and “Developer”. The “Admin” and “SU” roles cannot be modified or deleted.

3. Where can I keep track of my & my team’s past activities?

You can view all of your activities as well as your team’s under the “Activities” menu on the left-hand side of each solution.

4. My training pipeline is ready on Dev. How do I run it on Production?

xpresso.ai supports promotion of a pipeline from one instance to another. This action can be performed by a person with the appropriate role.

5. How do I reset my password?

If you forget your password, click the “Forgot Password” link on the login page to receive further instructions on resetting your password.

6. Can I use the sample projects directly?

Yes, every installation comes with a set of sample projects under the “Sample solutions” tab in Solution Overview once you login. Sample solutions have been created to demonstrate various features of xpresso.ai. These include:

• sample_project_basic - demonstrates the use of jobs, services, and databases

• sample_project_etl_bi - demonstrates an ETL pipeline, which fetches data from a text file, cleans it and stores it in a data warehouse, as well as a query service to query the data in the warehouse

• sample_project_ml - demonstrates machine learning pipelines, Inference Services, and A/B Testing

• sample_project_data_management - demonstrates a pipeline to fetch data from a file, explore it, and visualize the results, without writing a single line of code, by using xpresso.ai Marketplace components

• sample_project_spark - demonstrates a machine learning pipeline run on a Spark cluster

7. What are Components and Pipelines?

Component - A component is a piece of software that achieves a certain well-defined functionality and can be run on its own (usually as a Docker image). Components combine to form a solution. Components in xpresso.ai can be of the following six types: job, service, database, library, inference_service, and pipeline_job. All solutions can be combinations of these six types of components.

Pipeline - a pipeline is a collection of jobs (i.e., components of type “job” or “pipeline_job”) run in a particular sequence. Typically, pipelines are used to define Training Pipelines for Analytics solutions, which could include components such as “Fetch Data”, “Prepare Data”, “Extract Features”, “Train Model”, and “Validate Model”. Similarly, a pipeline can be used to define an ETL pipeline of a BI solution, with components such as “Extract Data”, “Transform Data”, and “Load Data”.

8. How can I make the best use of the same components under different pipelines?

Once a component is created, it can be used within any pipeline. When you create a pipeline, all components defined as “pipeline_job” or “job” will automatically appear on the right-hand side of the Solution Builder. You can simply drag and drop that component to the pipeline. The step can be repeated for the other pipelines.

9. How can I create a non-linear pipeline in xpresso.ai?

In xpresso, you can build complex and non-linear pipelines in the Solution Builder and execute them in Experiments. You can define each component to have up to three dependencies on other components. The steps to create a pipeline can be found here Link<link to documentation>.

10. How do I modify an existing solution?

To modify the basic information in the solution, click the “Modify” icon on the solution toolbar.

You can use the Solution Builder to modify the components and pipelines in the solution directly by drag and drop functionality.

11. How can I Clone an existing solution?

To create a copy of a solution, go to the solution “Workspace” page, and then click the “Clone” icon.

Please note: When a solution is cloned, its architecture is copied over to the new solution, but no code is copied. You must provide the code for the new solution from scratch (or you can copy the code for the old solution manually, if you have access to it).

12. How do I bring code from Jupyter notebooks to my code repo?

You should create a new component in your solution using the “jupyter” flavor. This will create a blank notebook for you with a few cells pre-populated with xpresso.ai skeleton code. You can open this notebook by using the “Edit Code” link after clicking the component in xpresso.ai Solution Builder. You can then write your code into these cells or add new cells, as required. After coding and testing the notebook, you can check it into the code repository by clicking the “Push” button on the customized notebook. You can then build and deploy the component, as usual.

13. Is Bitbucket supported in xpresso?

Yes, xpresso has a close integration with Git protocol-based repositories, like Bitbucket. When you create a new solution, or add a new component to a solution, xpresso automatically creates the solution repository with the appropriate folder structure and some skeleton code created for you to get started.

14. How do I use GitLab?

You can directly go to your solution repository by clicking on the GitLab icon on the top right side.

When you create a solution under xpresso and define components, it automatically creates the code repository within GitLab and provides a folder structure for all your components with some sample code to get you started. The sample code is dependent on the component type and the flavor you have chosen when building the solution.