Kód: 48007519
"Scalable Machine Learning Architectures: Best Practices for Handling Big Data and Distributed Systems" is the definitive guide for data scientists, machine learning engineers, and architects aiming to build and deploy machine lea ... celý popis
Angličtina
18.32 €
Bežne: 19.74 €
Ušetríte 1.42 €

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Anotácia knihy
"Scalable Machine Learning Architectures: Best Practices for Handling Big Data and Distributed Systems" is the definitive guide for data scientists, machine learning engineers, and architects aiming to build and deploy machine learning systems that can scale seamlessly with the demands of big data and modern distributed systems. In today's world of rapidly growing data volumes, creating scalable and efficient machine learning pipelines is critical to success.
This book provides a hands-on approach to designing machine learning architectures that are robust, efficient, and ready to handle real-world challenges. From implementing distributed training techniques to optimizing data pipelines, you'll learn how to leverage state-of-the-art tools and platforms such as TensorFlow, PyTorch, Apache Spark, Kubernetes, and more.
Through real-world examples and actionable strategies, "Scalable Machine Learning Architectures" equips you to address scalability issues, improve model performance, and ensure efficient resource utilization.
Inside this book, you'll learn how to:
Whether you're building recommendation systems, real-time prediction engines, or large-scale natural language processing applications, this book provides the roadmap to tackle the challenges of scaling machine learning in a data-intensive world.
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18.32 €
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