# Using the Aizen Jupyter Console Aizen provides a JupyterLab interface called the Aizen Jupyter console for you to manage ML jobs. ## Launching the Aizen Jupyter Console 1. In a browser, enter the application URL: `http:///aizen/console`. 2. In the **Sign in** dialog box, enter your username and password, and click the **Sign in** button. ![](/files/ZQh0fq6Zo93F3YTLz22q)\ \ This will launch the Aizen Jupyter console:

## Creating a Working Folder It is recommended that you create a working folder for your project's ML files and then launch the notebook within that folder. 1. To create a working folder, right-click on the folders pane on the left side and select **New Folder** from the drop-down menu:\ ![](/files/6lAAkGJM459HHXfCEBzp) 2. Enter a name for your project:\ ![](/files/16LdX7aa5kUVJqKKWzWI) ## Opening the Aizen Notebook 1. Open the project folder that you created by double-clicking on the folder name.\ ![](/files/LcVHnfFZkU2geqrb7zfj) 2. Click on the **Aizen (kernel)** notebook button to launch the notebook.\ ![](/files/ZowR5qb4JhIl6o09U2gG) \ The Aizen notebook interface will appear:

In the Aizen notebook, you can run commands to create an ML project and to manage your ML workflow, such as configuring data sources and training and serving an ML model. ## Creating a Project Create a project for your ML workflow. All your ML jobs execute under a project. Typically, you will create a single project for all ML workflows related to the same set of data sources. 1. To create a project, enter the `create project ` command in the code cell of the Aizen notebook:

2. Click the run icon in the toolbar to execute the command:

3. When the `create project` command finishes executing, you will see a `Created project` message indicating that the project was created.

The current working project is now set to the project that you just created. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://aizen-corp.gitbook.io/docs/getting-started/using-the-aizen-jupyter-console.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.