Book Review: MLOps with Red Hat OpenShift
Having previously enjoyed the authors’ “Machine Learning on Kubernetes,” I dove into “MLOps with Red Hat OpenShift” with high expectations, and it certainly delivered. Ross Brigoli and Faisal Masood offer a comprehensive guide that expertly bridges the gap between Machine Learning (ML) and Operations (Ops) within the OpenShift platform.
This article was originally published in techbeatly
Strengths:
- Solid Foundation: The book starts with a clear introduction to MLOps concepts, ensuring readers with even limited ML experience can grasp the fundamentals.
- OpenShift Focus: The chapters specifically delve into how to leverage OpenShift AI (formerly Red Hat OpenShift Data Science — RHODS) on various platforms like Red Hat OpenShift Service on AWS (ROSA). This targeted approach is valuable for those committed to the OpenShift ecosystem.
- Unique Insights: The content on GPU configurations is particularly noteworthy, addressing a topic often underrepresented in online resources.
- Practical Solutions: My existing doubts regarding ML model training, deployment, and operation were effectively addressed, providing a clear path to implementation.
Target Audience:
This book is ideal for anyone seeking a structured approach to kickstarting their Machine Learning journey with OpenShift. It caters well to developers, data scientists, and DevOps professionals looking to integrate MLOps practices into their OpenShift environment.
Overall Impression:
“MLOps with Red Hat OpenShift” lives up to its predecessor’s quality. It is a well-structured, informative resource that empowers readers to confidently navigate the complexities of deploying and managing ML models within OpenShift. My thanks to the authors for creating such valuable content and to Vinishka and Packt Publishing for providing the review copy.
This article was originally published in techbeatly
