Oracle Autonomous Data Warehouse Cloud Service (ADW), Part 5: Create Notebooks in Oracle Machine Learning

Juarez Junior
4 min readMar 1, 2019


Oracle Autonomous Database

by Juarez Junior


This is the fifth post in a series, where I explain the summarized steps that show how to use the Oracle Autonomous databases, with a special focus on tools and Machine Learning.

This post explains how to create and run Notebooks in Oracle Machine Learning. This blog post shows how to create a Notebook and run it in Oracle Machine Learning.

So without further ado, it’s time to perform the procedures.

Create Notebooks in Oracle Machine Learning

First, access your web console for Oracle Machine Learning in Autonomous Data Warehouse (ADW).

web console

What’s a Notebook?

An Oracle Machine Learning notebook in ADW is provided as a Graphical User Interface (GUI) - Web Console for data analysis, data discovery, and data visualization. Whenever a notebook is created, it must be defined with a specific Interpreter Settings specification.

The web console is the result of a Jupyter Notebook combined with a web component — Apache Zeppelin, so you have an enterprise-grade notebook to explore your data science, machine learning, and deep learning needs.

The notebook contains an internal list of bindings that determines the order of the interpreter bindings.

Click Notebooks on the home page of your web console.


You will see the Notebooks page.

Notebooks page

On the Notebooks page, click Create. The Create Notebook dialog box opens.

Create Notebook

The Create Notebook screen will be shown:

Create Notebook modal window

Provide the information for your notebook as below:

Create Notebook — notebook information

Click the OK button and after a few seconds, you will see the notebook with the command menu options available, ones that allow you to run the notebook for example.

As usual, note that you can also import Notebooks.

Import Notebook

The Jupyter notebook format (.ipnb, JSON)

Jupyter (IPython) notebook files are simple JSON documents, containing text, source code, rich media output, and metadata. each segment of the document is stored in a cell.

It is an ipynb file. Each .ipynb file is a text file that describes the contents of your notebook. In essence, it is expressed as a JSON file.

In case you need a full reference to learn and deep dive into Jupyter Notebook, please check the official documentation here and here.

The Notebook

Provide the information as explained below:

a. In the Name field, provide a name for the notebook;
b. In the Comments field, enter comments if any;
c. The Connection field specifies the Global connection group.

Click OK. This completes the task of creating a notebook. You must now open the notebook in the Notebook editor to set the interpreter bindings.

Click Back to return to the Notebooks page, and to save the changes in the notebook.

Wrap up

That’s it, your Notebook is created and ready to be run. The next post will show the steps to import data from an Excel file so that you can run a Jupyter Notebook with the data set. Stay tuned!

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Juarez Junior

Senior Principal #Java Developer Evangelist @ Oracle. Invite me to speak about #Java #OracleDatabase #OracleCloud #Blockchain #DevRel ☕️🥑