Oracle Autonomous Data Warehouse Cloud Service (ADW), Part 7: Run Notebooks with Oracle Machine Learning

Oracle Autonomous Database

by Juarez Junior

Introduction

This is the seventh 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, Data Science and Machine Learning.

This post explains how to run AI Notebooks with Oracle Machine Learning.

So without further ado, let’s start!

Run AI Notebooks with Oracle Machine Learning

As usual in data science, you must fetch data in a notebook from the desired data source, and run the notebook for data analysis and data visualization.

You can then use charts and apply machine learning algorithms as it might be required.

In order to run a notebook, perform as shown below:

1. Select the Notebooks option in the Quick Actions items as shown below:
2. Click the notebook that you want to run, here it is OracleEventLondon. The notebook opens in the Notebook editor.

Select Notebooks in Quick Actions

Click on your target Notebook — an example is OracleEventLondon

1. Type the SQL statement to fetch data from the Oracle Autonomous Database. For example, a querySELECT * from SH.SALES;where SH is the schema name and SALES ,the table name as shown in the screenshot below.
2. At last, click the Run button as shown below.

Run button

So as shown above, you can run SQL statements/scripts as well as call PL/SQL functions in packages as it might be required by your Data Science project.

After you run the notebook, it fetches the data into the notebook in the next paragraph.

A paragraph is a notebook component where you can write your SQL and PL/SQL call as well as run scripts.

A paragraph has an input section and an output section.

In the input section, you specify the interpreter to run along with the text. This information is sent to the interpreter to be executed.

In the output section, the results of the interpreter are provided.

Great analysis with charts

The output section of the paragraph has a charting component that displays the results as graphical output.

The chart interface allows you to interact with the output in the notebook paragraph. You have the option to run and edit single a paragraph or all paragraphs in a notebook.

You have a choice of many chart types such as Bar Chart, Pie Chart, Area Chart, Line Chart, and Scatter Chart, as you can see below:

Scatter Chart

Wrap up

That’s it, you’ve just run your first AI Notebook with Oracle Machine Learning.

The next post will show you how to apply some machine learning algorithms with Autonomous Database. Stay tuned!

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