Book Image

MLOps with Red Hat OpenShift

By : Ross Brigoli, Faisal Masood
Book Image

MLOps with Red Hat OpenShift

By: Ross Brigoli, Faisal Masood

Overview of this book

MLOps with OpenShift offers practical insights for implementing MLOps workflows on the dynamic OpenShift platform. As organizations worldwide seek to harness the power of machine learning operations, this book lays the foundation for your MLOps success. Starting with an exploration of key MLOps concepts, including data preparation, model training, and deployment, you’ll prepare to unleash OpenShift capabilities, kicking off with a primer on containers, pods, operators, and more. With the groundwork in place, you’ll be guided to MLOps workflows, uncovering the applications of popular machine learning frameworks for training and testing models on the platform. As you advance through the chapters, you’ll focus on the open-source data science and machine learning platform, Red Hat OpenShift Data Science, and its partner components, such as Pachyderm and Intel OpenVino, to understand their role in building and managing data pipelines, as well as deploying and monitoring machine learning models. Armed with this comprehensive knowledge, you’ll be able to implement MLOps workflows on the OpenShift platform proficiently.
Table of Contents (13 chapters)
Free Chapter
1
Part 1: Introduction
3
Part 2: Provisioning and Configuration
6
Part 3: Operating ML Workloads

Training a model using Red Hat ODS

Let’s build a simple model using Red Hat ODS. Recall Chapter 3, Building Machine Learning models with OpenShift, and create a new data science project named wines. Create a workbench named wines inside the project using the Standard Data Science notebook image and a Small container size. Create new persistence storage named wines with 20 GB of storage. There is no need to create a data connection at this stage. Once you create this project, you will have the following screen for your project:

Figure 4.8 – Red Hat data science project

  1. Now, launch the notebook and clone the accompanying Git repository of this book. Use the chapter4/wine-data-version.ipynb file to create a version of the wines.csv file in the Pachyderm repo. Note the commit ID while running this notebook.
  2. Once you have executed this notebook, open chapter4/wine-training.ipynb to train a simple linear regression model. Let’...