Scalable Machine Learning Architectures: Best Practices for Handling Big Data and Distributed Systems - Softcover
Inhaltsangabe
"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:
- Design end-to-end machine learning workflows that scale effortlessly.
- Implement distributed training across GPUs and TPUs for large datasets.
- Optimize data preprocessing with tools like Apache Spark and Hadoop.
- Deploy machine learning models on Kubernetes, Docker, and cloud platforms.
- Use feature stores and model registries to manage scalable pipelines.
- Monitor and maintain production-grade systems with ML observability tools.
- Handle challenges in big data environments, such as latency, fault tolerance, and data sharding.
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.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Suchergebnisse für Scalable Machine Learning Architectures: Best Practices...
Beispielbild für diese ISBN
Scalable Machine Learning Architectures (Paperback)
Print-on-DemandAnbieter: Grand Eagle Retail, Bensenville, IL, USA
Verkäuferbewertung 5 von 5 Sternen
Paperback. Zustand: new. Paperback. "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: Design end-to-end machine learning workflows that scale effortlessly.Implement distributed training across GPUs and TPUs for large datasets.Optimize data preprocessing with tools like Apache Spark and Hadoop.Deploy machine learning models on Kubernetes, Docker, and cloud platforms.Use feature stores and model registries to manage scalable pipelines.Monitor and maintain production-grade systems with ML observability tools.Handle challenges in big data environments, such as latency, fault tolerance, and data sharding.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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9798307714768
Beispielbild für diese ISBN
Scalable Machine Learning Architectures: Best Practices for Handling Big Data and Distributed Systems
Print-on-DemandAnbieter: California Books, Miami, FL, USA
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. Print on Demand. Bestandsnummer des Verkäufers I-9798307714768
Neu kaufen
EUR 21,11
Versand gratis
Versand innerhalb von USA
Versand innerhalb von USA
Anzahl: Mehr als 20 verfügbar
Beispielbild für diese ISBN
Scalable Machine Learning Architectures (Paperback)
Print-on-DemandAnbieter: CitiRetail, Stevenage, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Paperback. Zustand: new. Paperback. "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: Design end-to-end machine learning workflows that scale effortlessly.Implement distributed training across GPUs and TPUs for large datasets.Optimize data preprocessing with tools like Apache Spark and Hadoop.Deploy machine learning models on Kubernetes, Docker, and cloud platforms.Use feature stores and model registries to manage scalable pipelines.Monitor and maintain production-grade systems with ML observability tools.Handle challenges in big data environments, such as latency, fault tolerance, and data sharding.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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798307714768
Neu kaufen
EUR 25,87
EUR 42,26 shipping
Versand von Vereinigtes Königreich nach USA
Versand von Vereinigtes Königreich nach USA
Anzahl: 1 verfügbar
Foto des Verkäufers
Chesterfield, G: Scalable Machine Learning Architectures
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. Neuware - '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. Bestandsnummer des Verkäufers 9798307714768
Neu kaufen
EUR 31,00
EUR 61,79 shipping
Versand von Deutschland nach USA
Versand von Deutschland nach USA
Anzahl: 2 verfügbar