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Data Engineering for Machine Learning: Designing Robust Pipelines and Workflows
Format:
Paperback
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.62 kg
0.62 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Data Engineering for Machine Learning: Designing Robust Pipelines and WorkflowsMachine learning models are only as good as the data pipelines that power them. Data Engineering for Machine Learning is a practical guide that shows you how to build robust, scalable, and production-ready data pipelines specifically designed for ML workflows. From raw data ingestion to real-time deployment, this book walks you through each stage with hands-on examples, best practices, and real-world use cases.Whether you're building batch pipelines for model training or streaming architectures for real-time predictions, this book helps you bridge the gap between data engineering and machine learning with clarity and confidence. You'll learn how to handle large datasets, automate workflows with CI/CD, manage model artifacts, monitor data drift, and collaborate effectively with cross-functional teams.Summary: This book demystifies the complex ecosystem of ML data pipelines by focusing on practical solutions that work in production. You'll explore tools like Apache Airflow, MLflow, Spark, Kafka, and Kubernetes, and learn how to integrate them into clean, maintainable workflows. By the end, you’ll be equipped to build reliable systems that scale with your data and adapt to evolving business needs.Key Features of this Book:Step-by-step tutorials for designing ML-focused data pipelinesReal-world case studies including fraud detection and NLP workflowsPractical insights into CI/CD, version control, and pipeline automationIn-depth coverage of data handling, scaling, monitoring, and deploymentCheat sheets, code examples, and workflow templates for immediate useThis book is ideal for data engineers, ML engineers, and developers who want to build robust ML infrastructure. It’s also perfect for data scientists looking to understand pipeline architecture and anyone transitioning from traditional data processing into ML-ready systems.If you're ready to take your machine learning workflows from experimentation to production, Data Engineering for Machine Learning is your essential guide. Start building pipelines that scale, perform, and deliver real impact—grab your copy today!
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U$S 34,79
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