Scaling Graph Learning for the Enterprise | Production-Ready Graph Learning and Inference
Idioma: inglés
Editorial: O'Reilly Media, 2025
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Librería: preigu, Osnabrück, Alemaniapreigu
Vendedor de IberLibro desde 5 de agosto de 2024
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Scaling Graph Learning for the Enterprise | Production-Ready Graph Learning and Inference | Ahmed Menshawy (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2025 | O'Reilly Media | EAN 9781098146061 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
N° de ref. del artículo 133887888
- Título
- Scaling Graph Learning for the Enterprise | Production-Ready Graph Learning and Inference
- Autor
- Ahmed Menshawy (u. a.)
- Editorial
- O'Reilly Media
- Año de publicación
- 2025
- Estado
- Neu
- Encuadernación
- Taschenbuch
- Idioma
- inglés
- ISBN 10
- 1098146069
- ISBN 13
- 9781098146061
- Peso del artículo
- 585 gramos
- Dimensiones
- 233 x 178 x 19 mm
- Catálogos de vendedores
- Bücher
Tackle the core challenges related to enterprise-ready graph representation and learning. With this hands-on guide, applied data scientists, machine learning engineers, and practitioners will learn how to build an E2E graph learning pipeline. You'll explore core challenges at each pipeline stage, from data acquisition and representation to real-time inference and feedback loop retraining.
Drawing on their experience building scalable and production-ready graph learning pipelines, the authors take you through the process of building robust graph learning systems in a world of dynamic and evolving graphs.
- Understand the importance of graph learning for boosting enterprise-grade applications
- Navigate the challenges surrounding the development and deployment of enterprise-ready graph learning and inference pipelines
- Use traditional and advanced graph learning techniques to tackle graph use cases
- Use and contribute to PyGraf, an open source graph learning library, to help embed best practices while building graph applications
- Design and implement a graph learning algorithm using publicly available and syntactic data
- Apply privacy-preserving techniques to the graph learning process
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Acerca del autor
Ahmed is the coauthor of Deep Learning with TensorFlow and the author of Deep Learning by Example, focusing on advanced topics in deep learning.
Sameh is an expert in machine learning and health informatics. He has more than a decade of both academic and industrial experience in machine learning and artificial intelligence solutions. He obtained his PhD from the University of Galway, where he did research on machine learning on graphs and its applications in biomedical applications and a master's degree in cardiovascular intervention medicine.
He later worked for Mastercard, Carelon, and Microsoft in technical leadership roles where he built machine learning powered solutions in the domains of finance, healthcare insurance, and content generation. His contributions are mainly focused on the topics of representation learning, natural language processing, and health informatics.
Maraim Rizk Masoud is a leading machine learning engineer at Mastercard's Cyber and Intelligence division, concurrently serving as an AI researcher. With a diverse background spanning both industry and academia, Maraim has delved into various AI domains, including natural language processing and AI governance. She holds an MSc in Machine Learning from Imperial College London and an MEng from the University of Southampton.
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preigu
Osnabrück, Alemania
Vendedor de IberLibro desde 5 de agosto de 2024
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