Generative Ai With Kubernetes
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Generative AI with Kubernetes
DESCRIPTION Over the past few years, we have seen leaps and strides in ML and most recently generative AI. Companies and software teams are rushing to enhance, rebuild, and create new software offerings with this new intelligence. As they innovate and create delightful new experiences for their customers new challenges arise. Understanding how these applications work and how to use state-of-the-art infrastructure tools like Kubernetes will help organizations and professionals succeed with this new technology. The book covers essential technical implementations from ML fundamentals through advanced deployment strategies, focusing on practical patterns. Core topics include Kubernetes-native GPU scheduling and resource management, MLOps pipeline architectures using Kubeflow/MLflow, and advanced model serving patterns. It details data management architectures, vector databases, and RAG systems, alongside monitoring solutions with Prometheus/Grafana. Finally, we will look at some advanced concerns for production in the realm of security and data reliability. After reading this book, you will be equipped with a broad knowledge of the end-to-end generative AI pipeline and how Kubernetes can be leveraged to run your generative AI workloads at scale in the real-world. KEY FEATURES ● Learn how Kubernetes can help you run your generative AI workloads. ● Using hands-on examples, you will work with real-world foundational models and a variety of tools and capabilities in the K8s ecosystem. ● A broad survey of both generative AI and Kubernetes in one book. WHAT YOU WILL LEARN ● How to evaluate and compare models for new applications and use cases. ● How Kubernetes can add reliability and scale to your AI applications. ● What does an AI delivery pipeline contain and how to start one. ● How AI models encode words and work with natural language. ● How prompting and refinement techniques can improve results. ● How to use your own data to augment AI responses. WHO THIS BOOK IS FOR This book is for teams building new applications or new functionality with generative AI, but want to better understand the infrastructure needed to bring their AI applications to production. This book is also for shared services, infrastructure, or cybersecurity teams who provide platforms and infrastructure for application, or product development. TABLE OF CONTENTS 1. Introduction to Generative Artificial Intelligence 2. Kubernetes for Generative AI 3. Introduction to Foundational Models on Kubernetes 4. Working with Foundational Models 5. Process and Pipelines 6. Process and Pipelines on Kubernetes 7. Managing Data for Generative AI 8. Refining and Improving Results 9. Observability and Monitoring 10. Securing ML/GenAI Pipelines on K8s
Kubernetes for Generative AI Solutions
Author: Ashok Srirama
language: en
Publisher: Packt Publishing Ltd
Release Date: 2025-06-06
Master the complete Generative AI project lifecycle on Kubernetes (K8s) from design and optimization to deployment using best practices, cost-effective strategies, and real-world examples. Key Features Build and deploy your first Generative AI workload on Kubernetes with confidence Learn to optimize costly resources such as GPUs using fractional allocation, Spot Instances, and automation Gain hands-on insights into observability, infrastructure automation, and scaling Generative AI workloads Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionGenerative AI (GenAI) is revolutionizing industries, from chatbots to recommendation engines to content creation, but deploying these systems at scale poses significant challenges in infrastructure, scalability, security, and cost management. This book is your practical guide to designing, optimizing, and deploying GenAI workloads with Kubernetes (K8s) the leading container orchestration platform trusted by AI pioneers. Whether you're working with large language models, transformer systems, or other GenAI applications, this book helps you confidently take projects from concept to production. You’ll get to grips with foundational concepts in machine learning and GenAI, understanding how to align projects with business goals and KPIs. From there, you'll set up Kubernetes clusters in the cloud, deploy your first workload, and build a solid infrastructure. But your learning doesn't stop at deployment. The chapters highlight essential strategies for scaling GenAI workloads in production, covering model optimization, workflow automation, scaling, GPU efficiency, observability, security, and resilience. By the end of this book, you’ll be fully equipped to confidently design and deploy scalable, secure, resilient, and cost-effective GenAI solutions on Kubernetes.What you will learn Explore GenAI deployment stack, agents, RAG, and model fine-tuning Implement HPA, VPA, and Karpenter for efficient autoscaling Optimize GPU usage with fractional allocation, MIG, and MPS setups Reduce cloud costs and monitor spending with Kubecost tools Secure GenAI workloads with RBAC, encryption, and service meshes Monitor system health and performance using Prometheus and Grafana Ensure high availability and disaster recovery for GenAI systems Automate GenAI pipelines for continuous integration and delivery Who this book is for This book is for solutions architects, product managers, engineering leads, DevOps teams, GenAI developers, and AI engineers. It's also suitable for students and academics learning about GenAI, Kubernetes, and cloud-native technologies. A basic understanding of cloud computing and AI concepts is needed, but no prior knowledge of Kubernetes is required.
Keras to Kubernetes
Build a Keras model to scale and deploy on a Kubernetes cluster We have seen an exponential growth in the use of Artificial Intelligence (AI) over last few years. AI is becoming the new electricity and is touching every industry from retail to manufacturing to healthcare to entertainment. Within AI, were seeing a particular growth in Machine Learning (ML) and Deep Learning (DL) applications. ML is all about learning relationships from labeled (Supervised) or unlabeled data (Unsupervised). DL has many layers of learning and can extract patterns from unstructured data like images, video, audio, etc. em style="box-sizing: border-box;"Keras to Kubernetes: The Journey of a Machine Learning Model to Production takes you through real-world examples of building DL models in Keras for recognizing product logos in images and extracting sentiment from text. You will then take that trained model and package it as a web application container before learning how to deploy this model at scale on a Kubernetes cluster. You will understand the different practical steps involved in real-world ML implementations which go beyond the algorithms. Find hands-on learning examples Learn to uses Keras and Kubernetes to deploy Machine Learning models Discover new ways to collect and manage your image and text data with Machine Learning Reuse examples as-is to deploy your models Understand the ML model development lifecycle and deployment to production If youre ready to learn about one of the most popular DL frameworks and build production applications with it, youve come to the right place!