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.

Related Content you might be interested in

Current Title:

Small book image
MLOps with Red Hat OpenShift
Ross Brigoli | Faisal Masood
Jan, 2024 | 238 pages
Small book image
Machine Learning on Kubernetes
Faisal Masood | Ross Brigoli
Jun, 2022 | 384 pages
Small book image
OpenShift Multi-Cluster Management Handbook
Giovanni Fontana | Rafael Pecora
Nov, 2022 | 458 pages
Small book image
IBM Cloud Pak for Data
Sriram Srinivasan | Hemanth Manda | Deepak Rangarao
Nov, 2021 | 336 pages
Table of Contents (13 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
Share Your Thoughts
Download a free PDF copy of this book
Free Chapter
1
Part 1: Introduction
Part 1: Introduction
2
Chapter 1: Introduction to MLOps and OpenShift
Chapter 1: Introduction to MLOps and OpenShift
What is MLOps?
Introduction to OpenShift
Understanding operators
Understanding how OpenShift supports MLOps
Red Hat OpenShift Data Science (RHODS)
The advantages of the cloud
ROSA
Summary
References
3
Part 2: Provisioning and Configuration
Part 2: Provisioning and Configuration
4
Chapter 2: Provisioning an MLOps Platform in the Cloud
Chapter 2: Provisioning an MLOps Platform in the Cloud
Technical requirements
Installing OpenShift on AWS
Installing Red Hat ODS
Installing partner software on RedHat ODS
Installing Pachyderm
Summary
5
Chapter 3: Building Machine Learning Models with OpenShift
Chapter 3: Building Machine Learning Models with OpenShift
Technical requirements
Using Jupyter Notebooks in OpenShift
Provisioning an S3 store
Using ML frameworks in OpenShift
Using GPU acceleration for model training
Enabling GPU support
Building custom notebooks
Summary
6
Part 3: Operating ML Workloads
Part 3: Operating ML Workloads
7
Chapter 4: Managing a Model Training Workflow
Chapter 4: Managing a Model Training Workflow
Technical requirements
Configuring Pachyderm
Versioning your data with Pachyderm
Training a model using Red Hat ODS
Building a model training pipeline
Summary
8
Chapter 5: Deploying ML Models as a Service
Chapter 5: Deploying ML Models as a Service
Packaging and deploying models as a service
Autoscaling the deployed models
Releasing new versions of the model
Securing model endpoints
Summary
9
Chapter 6: Operating ML Workloads
Chapter 6: Operating ML Workloads
Monitoring ML models
Installing and configuring Prometheus and Grafana
Logging inference calls
Optimizing cost
Summary
References
10
Chapter 7: Building a Face Detector Using the Red Hat ML Platform
Chapter 7: Building a Face Detector Using the Red Hat ML Platform
Architecting a human face detector system
Training a model for face detection
Installing Redis on Red Hat OpenShift
Building and deploying the inferencing application
Bringing it all together
Optimizing cost for your ML platform
Summary
11
Index
Index
Why subscribe?
12
Other Books You May Enjoy
Other Books You May Enjoy
Packt is searching for authors like you
Share Your Thoughts
Download a free PDF copy of this book

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “Create a user named admin.”

A block of code is set as follows:

storage:
backend: MINIO
minio:
bucket: pachyderm

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

curl -O -L https://mirror.openshift.com/pub/openshift-v4/client/rosa/latest/rosa-linux.tar.gz
tar -xvzf rosa-linux.tar
echo PATH=$PATH:/home/cloudshell-user >> ~/.bashrc

Any command-line input or output is written as follows:

echo <the rendered yaml string> | oc apply -f-

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “Click on the Increase service quotas button if applicable to your cluster.”

Tips or important notes

Appear like this.

Previous Section
Next Section