Kubernetes for AI and Data Engineering: Build Scalable Pipelines, Train Faster, and Deploy Production-Ready ML Systems
AI workloads are growing faster than the systems meant to support them. Teams everywhere feel the pressure: bigger datasets, heavier models, tighter deadlines, and infrastructure that strains under the weight of real-world demands. What if you could run ETL pipelines, distributed training jobs, feature stores, vector search, and real-time inference on one unified platform, built for scale, speed, and reliability?
Kubernetes for AI and Data Engineering shows you how to make that possibility real. This book gives you a practical, end-to-end blueprint for building high-performance AI systems on Kubernetes, covering everything from data ingestion and batch processing to LLM fine-tuning, GPU scheduling, model serving, observability, and multi-team governance. No abstraction. No fluff. Just the patterns, templates, and strategies used in modern, production-grade AI platforms.
Inside, you'll learn how to:
Run ETL and feature engineering pipelines using Spark, Ray, and Kubernetes Jobs.
Train models faster with distributed GPU orchestration using Kubeflow, PyTorchJob, and advanced schedulers.
Deploy resilient inference services with KServe, TensorRT, and low-latency batching strategies.
Manage high-throughput streaming pipelines with Kafka, Flink, and scalable event-driven inference.
Build retrieval-augmented generation workflows with vector databases and LLM serving.
Implement security, cost control, and governance for multi-tenant AI clusters.
Design a self-service developer experience with golden paths, templates, and internal portals.
Every chapter delivers concrete, actionable knowledge you can apply immediately, whether you're a data scientist seeking independence, a machine learning engineer pushing toward production, or a platform/SRE/DevOps engineer responsible for scaling GPU-heavy workloads without blowing the budget.
If you're ready to build AI pipelines that scale smoothly, train models efficiently, and deploy systems that keep up with real users and real constraints, this book gives you the tools and patterns to make it happen.
Take the next step, equip yourself with the architecture, clarity, and confidence to build world-class AI systems on Kubernetes today.
