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Kubernetes for Machine Learning & Data Engineering: Build, Scale, and Automate End-to-End ML Pipelines and Data Workflows in the Cloud
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Publisher: Independently Published
Published: Nov 7 2025
Pages: 218
Weight: 0.85
Height: 0.46 Width: 7.00 Depth: 10.00
Language: English
Kubernetes for Machine Learning & Data Engineering: Build, Scale, and Automate End-to-End ML Pipelines and Data Workflows in the Cloud
How do you take a brilliant machine learning model or data pipeline from your laptop to production-where it runs reliably, scales automatically, and integrates seamlessly with your organization's systems? For many teams, that question defines the boundary between experimentation and real-world impact. Kubernetes for Machine Learning & Data Engineering bridges that gap with a modern, hands-on roadmap for building, scaling, and automating ML and data workflows in the cloud.This book delivers practical, implementation-focused guidance for engineers, data scientists, and platform architects who want to harness the power of Kubernetes for complex workloads. You'll learn how to containerize ML and data applications, orchestrate distributed training and inference, manage pipelines, monitor resources, and scale intelligently-all using the same platform trusted by the world's most demanding production systems.
Every chapter translates advanced Kubernetes concepts into concrete, reproducible workflows. You'll move from deploying your first ML job to managing multi-cluster architectures, integrating Airflow, Kubeflow, and MLflow, and optimizing GPU/TPU utilization for maximum performance and cost efficiency. Real examples, working manifests, and complete end-to-end architectures guide you at each step, helping you build systems that are not only functional-but future-proof.
By the end of this book, you'll be able to:
Build and containerize reproducible data pipelines and ML training workflows
Automate feature processing, model training, and inference using Kubernetes Jobs, CronJobs, and Operators
Implement distributed frameworks like Spark, Dask, and Ray on Kubernetes for large-scale data processing
Deploy and autoscale ML services using KServe, Seldon Core, and serverless architectures
Manage infrastructure as code with Helm, Kustomize, and Terraform
Monitor, troubleshoot, and optimize performance and costs across clusters
Design hybrid and multi-cloud architectures for portable, resilient workloads
Whether you're modernizing legacy systems or designing your organization's next-generation ML platform, this book will help you turn Kubernetes from an infrastructure tool into a strategic advantage.
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