Prashanth Josyula

Essential Kubeflow: Engineering ML Workflows on Kubernetes equips readers with the tools to transform ML workflows from experimental notebooks to production-ready platforms with this comprehensive guide to Kubeflow, one of the most widely adopted open source MLOps platforms used to automate ML workloads. Whether you're a Machine Learning engineer looking to operationalize models, a platform engineer diving into ML infrastructure, or a technical leader architecting ML systems, this book provides practical solutions for real-world challenges. Through hands-on examples and production-tested patterns, readers will master essential skills for building enterprise-grade Machine Learning platforms: architecting production systems on Kubernetes, designing end-to-end ML pipelines, implementing robust model serving, scaling workloads efficiently, managing multi-user environments, deploying automated MLOps workflows, and integrating with existing ML tools. By the end of this book, readers will have the expertise to build and maintain scalable ML platforms that can handle the demands of modern enterprise AI initiatives.

mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt;

DESCRIPTIONLarge language models (LLMs) like GPT, BERT, and T5 are revolutionizing how we interact with technology - powering virtual assistants, content generation, and data analysis. As their influence grows, understanding their architecture, capabilities, and ethical considerations is more important than ever. This book breaks down the essentials of LLMs and explores retrieval-augmented generation (RAG), a powerful approach that combines retrieval systems with generative AI for smarter, faster, and more reliable results. It provides a step-by-step approach to building advanced intelligent systems that utilize an innovative technique known as the RAG thus making them factually correct, context-aware, and sustainable. You will start with foundational knowledge - understanding architectures, training processes, and ethical considerations - before diving into the mechanics of RAG, learning how retrievers and generators collaborate to improve performance. The book introduces essential frameworks like LangChain and LlamaIndex, walking you through practical implementations, troubleshooting, and optimization techniques. It explores advanced optimization techniques, and offers hands-on coding exercises to ensure practical understanding. Real-world case studies and industry applications help bridge the gap between theory and implementation. By the final chapter, you will have the skills to design, build, and optimize RAG-powered applications - integrating LLMs with retrieval systems, creating custom pipelines, and scaling for performance. WHAT YOU WILL LEARN● Understand the fundamentals of LLMs.● Explore RAG and its key components.● Build GenAI applications using LangChain and LlamaIndex frameworks.● Optimize retrieval strategies for accurate and grounded AI responses.● Deploy scalable, production-ready RAG pipelines with best practices.● Troubleshoot and fine-tune RAG pipelines for optimal performance. WHO THIS BOOK IS FORThis book is for AI practitioners, data scientists, students, and developers looking to implement RAG using LangChain and LlamaIndex. Readers having basic knowledge of Python, ML concepts, and NLP fundamentals would be able to leverage the knowledge gained to accelerate their careers.