MLOps with Red Hat OpenShift
A cloud-native approach to machine learning operations
Created by Ross Brigoli, Faisal Masood
Explore a cloud-native approach to machine learning operations using Red Hat OpenShift. Gain practical experience with MLOps workflows, from data preparation to model deployment, while working with tools like OpenShift Data Science, Pachyderm, and Intel OpenVino. Build the skills needed to manage and scale machine learning projects in real-world environments.
Packt | Jan 2024 | 238 min
What You Will Learn
You will move from foundational MLOps concepts to hands-on projects that use OpenShift and its partner tools. Through practical exercises, you will set up data pipelines, train models, and deploy them in a cloud-native environment. Each step builds your ability to operationalize and manage machine learning at scale.
Key Features
- Understand and apply core MLOps concepts for efficient machine learning workflows
- Provision and configure Red Hat OpenShift Data Science for real project needs
- Build, deploy, and monitor machine learning models using modern cloud-native tools
Target Audience
Designed for MLOps and DevOps engineers, data architects, and data scientists who want to deepen their OpenShift expertise. If you have a working knowledge of machine learning and want to streamline model deployment and operations, you will benefit from this course. Developers aiming to automate and scale ML workflows on OpenShift will also find it valuable.
