AI Notebooks - Tutorial - Create your first Machine Learning model

How to build your first Machine Learning model thanks to Miniconda

Last updated 6th June, 2022.


This tutorial will allow you to create your first OVHcloud AI notebook based on a very simple Machine Learning model: the simple linear regression.

At the end of this tutorial, you will have learned to master OVHcloud AI Notebooks and be able to predict the scores obtained by students as a function of the number of hours worked.

We will be able to predict a student's exam score based on the amount of time he has studied using a dataset available on Kaggle: Students Score Dataset.



You can launch your notebook from the OVHcloud Control Panel or via the ovhai CLI.

Launching a Jupyter notebook with "Miniconda" via UI

To launch your notebook from the OVHcloud Control Panel, refer to the following steps.

Code editor

Choose the Jupyterlab code editor.


In this tutorial, the Miniconda framework is used.

With Miniconda, you will be able to set up your environment by installing the Python libraries you need.

You can choose the conda version you want.

The default version of conda is functional for this tutorial.


You can choose the number of CPUs or GPUs you want.

Here, using 1 CPU is sufficient.

Launching a Jupyter notebook with "Miniconda" via CLI

If you want to launch it with the CLI, choose the jupyterlab editor and the conda framework.

To access the different versions of conda available, run the following command.

ovhai capabilities framework list -o yaml

If you do not specify a version, your notebook starts with the default version of conda.

Choose the number of CPUs (<nb-cpus>) to use in your notebook and use the following command.

ovhai notebook run conda jupyterlab \
    --name <notebook-name> \
    --framework-version <conda-version> \
    --cpu <nb-cpus>

You can then reach your notebook’s URL once the notebook is running.

Accessing the notebook

Once the repository has been cloned, find your notebook by following this path: ai-training-examples > notebooks > getting-started > miniconda > ai-notebooks-introduction > notebook-introduction-linear-regression.ipynb.

A preview of this notebook can be found on GitHub here.


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