Chapter 6. Tutorials for data scientists

To help you get started quickly, you can access learning resources for Red Hat OpenShift AI and its supported applications.

The OpenShift AI tutorial: Fraud detection example provides step-by-step guidance for using RHOAI to develop and train an example model in Jupyter notebooks, deploy the model, integrate the model into a fraud detection application, and refine the model by using automated pipelines.

Additonal resources are available on the Resources tab of the Red Hat OpenShift AI user interface.

Table 6.1. Tutorials

Resource NameDescription

Accelerating scientific workloads in Python with Numba

Watch a video about how to make your Python code run faster.

Building interactive visualizations and dashboards in Python

Explore a variety of data across multiple notebooks and learn how to deploy full dashboards and applications.

Building machine learning models with scikit-learn

Learn how to build machine learning models with scikit-learn for supervised learning, unsupervised learning, and classification problems.

Building a binary classification model

Train a model to predict if a customer is likely to subscribe to a bank promotion.

Choosing Python tools for data visualization

Use the website to help you decide on the best open source Python data visualization tools for you.

Exploring Anaconda for data science

Learn about Anaconda, a freemium open source distribution of the Python and R programming languages.

Getting started with Pachyderm concepts

Learn Pachyderm’s main concepts by creating pipelines that perform edge detection on a few images.

GPU Computing in Python with Numba

Learn how to create GPU accelerated functions using Numba.

Run a Python notebook to generate results in IBM Watson OpenScale

Run a Python notebook to create, train, and deploy a machine learning model.

Running an AutoAI experiment to build a model

Watch a video about building a binary classification model for a marketing campaign.

Training a regression model in Pachyderm

Learn how to create a sample housing data repository using a Pachyderm cluster to run experiments, analyze data, and set up regression.

Using Dask for parallel data analysis

Analyze medium-sized datasets in parallel locally using Dask, a parallel computing library that scales the existing Python ecosystem.

Using Jupyter notebooks in Watson Studio

Watch a video about working with Jupyter notebooks in Watson Studio.

Using Pandas for data analysis in Python

Learn how to use pandas, a data analysis library for the Python programming language.

Table 6.2. Quick start guides

Resource NameDescription

Creating a Jupyter notebook

Create a Jupyter notebook in JupyterLab.

Creating an Anaconda-enabled Jupyter notebook

Create an Anaconda-enabled Jupyter notebook and access Anaconda packages that are curated for security and compatibility.

Deploying a model with Watson Studio

Import a notebook in Watson Studio and use AutoAI to build and deploy a model.

Deploying a sample Python application using Flask and OpenShift

Deploy your data science model out of a Jupyter notebook and into a Flask application to use as a development sandbox.

Importing Pachyderm Beginner Tutorial Notebook

Load Pachyderm’s beginner tutorial notebook and learn about Pachyderm’s main concepts such as data repositories, pipelines, and using the pachctl CLI from your cells.

Querying data with Starburst Galaxy

Learn to query data by using Starburst Galaxy from a Jupyter notebook.

Securing a deployed model using Red Hat OpenShift API Management

Protect a model service API using Red Hat OpenShift API Management.

Using the Intel® oneAPI AI Analytics Toolkit (AI Kit) Notebook

Run a data science notebook sample with the Intel® oneAPI AI Analytics Toolkit.

Using the OpenVINO toolkit

Quantize an ONNX computer vision model using the OpenVINO model optimizer and use the result for inference from a notebook.

Table 6.3. How to guides

Resource NameDescription

How to choose between notebook runtime environment options

Explore available options for configuring your notebook runtime environment.

How to clean, shape, and visualize data

Learn how to clean and shape tabular data using IBM Watson Studio data refinery.

How to create a connection to access data

Learn how to create connections to various data sources across the platform.

How to create a deployment space

Learn how to create a deployment space for machine learning.

How to create a notebook in Watson Studio

Learn how to create a basic Jupyter notebook in Watson Studio.

How to create a project in Watson Studio

Learn how to create an analytics project in Watson Studio.

How to create a project that integrates with Git

Learn how to add assets from a Git repository into a project.

How to install Python packages on your notebook server

Learn how to install additional Python packages on your notebook server.

How to load data into a Jupyter notebook

Learn how to integrate data sources into a Jupyter notebook by loading data.

How to serve a model using OpenVINO Model Server

Learn how to deploy optimized models with the OpenVINO Model Server using OpenVINO custom resources.

How to set up Watson OpenScale

Learn how to track and measure outcomes from models with OpenScale.

How to update notebook server settings

Learn how to update the settings or the notebook image on your notebook server.

How to use data from Amazon S3 buckets

Learn how to connect to data in S3 Storage using environment variables.

How to view installed packages on your notebook server

Learn how to see which packages are installed on your running notebook server.

6.1. Accessing tutorials

You can access learning resources for Red Hat OpenShift AI and supported applications.


  • Ensure that you have logged in to Red Hat OpenShift AI.
  • You have logged in to the OpenShift web console.


  1. On the Red Hat OpenShift AI home page, click Resources.

    The Resources page opens.

  2. Click Access tutorial on the relevant tile.


  • You can view and access the learning resources for Red Hat OpenShift AI and supported applications.

Additional resources