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Data Pipelines in Machine Learning: Building End-to-End Systems for Data Ingestion, Training, Deployment, and Monitoring at Scale
Paperback
Publisher: Independently Published
Published: Sep 25 2025
Pages: 128
Weight: 0.52
Height: 0.27 Width: 7.00 Depth: 10.00
Language: English
Data Pipelines in Machine Learning: Building End-to-End Systems for Data Ingestion, Training, Deployment, and Monitoring at Scale
Unlock the power of machine learning by mastering the backbone of every AI system scalable, reliable, and production-ready data pipelines.
Machine learning models are only as strong as the pipelines that support them. From messy raw data to real-time deployment and monitoring, robust pipelines are what turn research prototypes into business-critical applications. Data Pipelines in Machine Learning provides a comprehensive, hands-on guide to designing, building, and scaling end-to-end ML workflows.
This book bridges the gap between data science experimentation and real-world production systems. It covers ingestion, preprocessing, feature engineering, training, deployment, monitoring, and continuous improvement, with clear explanations and practical examples throughout. Whether you are an ML engineer, data scientist, or architect, you will gain the skills to build resilient pipelines that operate reliably at scale in dynamic environments.
Benefits:Practical end-to-end coverage of ML pipelines from raw data ingestion to post-deployment monitoring.
Modern architectures and tools, including serverless, cloud-native, microservices, and vector database integrations.
Hands-on code examples in Python that demonstrate scalable and fault-tolerant pipeline patterns.
Best practices for governance, compliance, and security to ensure trust and regulatory alignment.
Case studies and real-world scenarios to illustrate challenges and solutions in global ML systems.
Take your machine learning projects from concept to production with confidence. Get your copy of Data Pipelines in Machine Learning today and start building scalable, reliable ML systems at scale.
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