Overview

In a world where machine learning models are only as effective as the data pipelines that feed them, this book delivers the practical knowledge you need to architect, build, and maintain high-quality data pipelines at scale. Whether you're working in the cloud, on-premises, or in hybrid environments, this book equips you to move from brittle scripts and ad-hoc processes to production-grade, automated ML pipelines. Written by a seasoned machine learning engineer with real-world experience across production systems and MLOps platforms, this book bridges the gap between theory and operational excellence. It distills hard-earned lessons and scalable patterns into a clear, actionable guide tailored for today's data and AI professionals. About the Technology: Modern ML systems demand far more than just models-they require robust data pipelines that are fault-tolerant, observable, testable, and repeatable. From data ingestion and real-time processing to model deployment and monitoring, pipeline engineering is now the backbone of successful AI products. This book explores proven technologies like Airflow, Spark, Kafka, MLflow, Kubernetes, Prefect, and Docker in a cohesive and production-ready context. What's Inside: Building batch and streaming ML pipelines from the ground up Integrating model training, validation, and deployment into end-to-end workflows Scheduling, parameterization, and orchestrating jobs with Airflow and PrefectR Real-time processing with Kafka, Spark, and windowed streams Observability: logging, tracing, metrics, and alerting for pipelines Testing strategies for unit, integration, and data quality assurance Scaling and cost optimization in cloud-native environments A complete project: ML churn prediction pipeline built and deployed step by step Every chapter includes real-world examples, working code, and practical best practices grounded in modern engineering principles. Who This Book Is For: If you're a machine learning engineer, data engineer, MLOps practitioner, or backend developer looking to productionize AI models and streamline pipeline workflows-this book is for you. It's designed for readers with Python experience and a basic understanding of ML concepts, but all necessary tools and techniques are explained from first principles. As ML adoption accelerates across industries, the need for scalable, resilient data pipelines is growing faster than ever. Organizations are actively hiring engineers who can bridge the gap between experimentation and deployment. Staying relevant means mastering this skillset now-not later. In just a few focused sessions, you'll go from fragmented scripts to cohesive, reusable pipelines ready for real-world deployment. This isn't a theoretical read-it's an engineering guide that pays off immediately. This book isn't just about writing code. It's about architecting intelligent infrastructure. You're not learning isolated tricks-you're acquiring a reusable engineering framework that applies across tools, clouds, and organizations. If you're ready to stop duct-taping your ML processes and start building systems that scale, buy this book today. Equip yourself with the technical patterns, practical tools, and deployment workflows that define modern machine learning engineering.

Full Product Details

9798262205769

Table of Contents

Reviews

Author Information

Tab Content 6

Countries Available