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.
Strengths:
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.
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Gineesh Madapparambath
Gineesh Madapparambath is the founder of techbeatly and he is the co-author of The Kubernetes Bible, Second Edition. and the author of ๐๐ป๐๐ถ๐ฏ๐น๐ฒ ๐ณ๐ผ๐ฟ ๐ฅ๐ฒ๐ฎ๐น-๐๐ถ๐ณ๐ฒ ๐๐๐๐ผ๐บ๐ฎ๐๐ถ๐ผ๐ป.
He has worked as a Systems Engineer, Automation Specialist, and content author. His primary focus is on Ansible Automation, Containerisation (OpenShift & Kubernetes), and Infrastructure as Code (Terraform).
(aka Gini Gangadharan - iamgini.com)
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