Building Cloud-Native Machine Learning Pipelines with Kubeflow: Orchestrating End-to-End AI Workflows, Model Training, and Serving on Kubernetes for Scalable Machine Learning Operations

by Greyson Chesterfield

Building Cloud-Native Machine Learning Pipelines with Kubeflow is the ultimate guide for developers, data scientists, and engineers aiming to leverage the full potential of Kubernetes for AI operations. From foundational knowledge of cloud-native architecture to orchestrating end-to-end AI workflows, this book offers a comprehensive roadmap for deploying scalable machine learning solutions in a cloud-native environment. Unlock the power of Kubeflow to manage model training, hyperparameter tuning, and real-time serving with ease. Discover how to set up your Kubernetes environment, manage data, automate workflows, and implement MLOps with industry-best practices. This hands-on guide ensures you stay ahead in the evolving field of AI, achieving both operational efficiency and model excellence.

Dive into practical examples, case studies, and step-by-step instructions to streamline ML pipelines while focusing on scalability, reproducibility, and continuous delivery. This is the definitive resource for transforming your approach to machine learning in the cloud—ideal for practitioners aiming to deploy robust ML systems with confidence. Whether you're a beginner or seasoned professional, this book equips you with the insights and tools to elevate your ML infrastructure.

123 pages, Kindle Edition

Published October 25, 2024