博客來Artificial intelligence systems today are driven by data at unprecedented scale. As machine learning, real-time inference, and generative AI reshape industries, organizations need robust big data platforms to ingest, process, and operationalize vast and complex datasets. Big data has become the backbone of modern AI systems, making data engineering skills essential for professionals across technology, analytics, and AI roles.
This book provides a practical guide to designing and building data platforms that power AI applications. It covers core big data technologies such as Hadoop, Spark, Kafka, NoSQL, and cloud data platforms, then connects them to the AI lifecycle, including data ingestion, feature engineering, scalable model training, real-time inference, and MLOps. Real-world use cases across finance, healthcare, e-commerce, and autonomous systems demonstrate how these technologies work together in production environments.
By the end of this book, the readers will be equipped to design end-to-end big data pipelines, support scalable AI and ML workloads, and extract insights from data at any velocity or volume. Whether you are a data engineer, ML practitioner, or architect, this book prepares you to build and operate AI-ready data systems with confidence.
What you will learn
● Design scalable big data platforms for AI systems.
● Process streaming and batch data at scale.
● Apply cloud-native architectures for data and AI.
● Engineer features and train models at scale.
● Deploy models with real-time inference and MLOps.
● Govern data security, privacy, and compliance at scale.
Who this book is for
This book is aimed at intermediate level professionals working with data and enterprise systems who want to apply big data technologies in real-world AI projects. It is well suited for data engineers, ML practitioners, software engineers, architects, and IT professionals building scalable AI-driven data platforms.
Table of Contents
1. Introduction to Big Data and AI integration
2. Big Data Storage and NoSQL Databases
3. Distributed Batch Processing with MapReduce and Apache Spark
4. Real-time Data Streaming and Analytics
5. Cloud-based Big Data Platforms
6. Data Ingestion, Preparation, and Feature Engineering
7. Scalable Machine Learning Model Training
8. Model Deployment and Real-time Inference
9. MLOps and Pipeline Automation
10. Big Data in Finance and FinTech
11. Big Data in Healthcare and Biomedicine
12. Big Data in E-commerce and Marketing
13. Big Data in IoT and Autonomous Systems
14. Data Governance, Security, and Privacy
15. Emerging Trends and Future Outlook
