AI-Driven Data Engineering and Machine Learning Operation at Scale

In the era of big data and artificial intelligence, organizations are increasingly relying on data-driven decision-making to fuel innovation and business growth. However, the scale and complexity of modern data systems present a significant challenge for data engineers and machine learning practitioners alike.

This book explores the powerful intersection of data engineering and machine learning operations (MLOps), where AI-driven techniques are transforming how data is collected, processed, and applied in real-world applications. As data pipelines grow in scale and sophistication, automation, efficiency, and scalability become essential to success.

AI-Driven Data Engineering and Machine Learning Operation at Scale provides a thorough and practical guide to designing intelligent data systems. You'll explore the tools and techniques used to automate data pipelines, maintain data quality, govern data access, and streamline the deployment of machine learning models into production environments.

Through a rich combination of theory, architecture patterns, and real-world examples, this book equips engineers and data professionals with the skills to build robust and scalable solutions. Topics include data orchestration, model versioning, explainability, performance monitoring, and end-to-end AI workflows.

Whether you're building a small prototype or supporting enterprise-scale AI platforms, this book will help you leverage AI to enhance every phase of your data lifecycle — from ingestion to prediction. It's your essential guide to succeeding in the complex, fast-moving world of modern data and AI engineering.

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