Overview

Unlock the full power of AI in production with Kubernetes the backbone of modern, scalable machine learning systems. Machine learning doesn't end when a model is trained. The real challenge begins when you deploy, scale, and monitor it in the real world. Kubernetes for Machine Learning: Deploying, Scaling, and Monitoring AI Models in Production is your practical guide to building reliable, high-performance ML systems using the industry's most trusted orchestration platform. This book breaks down the complexities of Kubernetes into clear, actionable steps tailored specifically for AI engineers, data scientists, DevOps teams, and MLOps practitioners. From containerizing ML workloads to automating pipelines, optimizing GPU usage, handling model rollouts, and observing real-time performance, you'll learn how to create production environments that can handle modern AI demands with ease. Whether you're running deep learning models, large language models, or high-throughput inference services, this guide equips you with the tools and patterns needed to ship models confidently and keep them running smoothly at scale. Benefits: End-to-end MLOps workflow: Learn how to package, deploy, and manage ML models in Kubernetes clusters. Scalable infrastructure: Master autoscaling, GPU scheduling, load balancing, and resource optimization for AI workloads. Production-grade monitoring: Implement logging, tracing, model performance tracking, and drift detection with modern observability tools. Real-world patterns: Follow proven architectures for serving APIs, batch jobs, streaming pipelines, and multi-model systems. Cloud and hybrid-ready: Apply concepts across AWS, GCP, Azure, on-prem Kubernetes, or hybrid setups. Ready to take your machine learning projects from experimentation to reliable production? Get your copy now and start building scalable, automated, and monitored AI systems with Kubernetes.

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