Scaling Graph Learning Enterprise by Menshawy Ahmed (27 results)

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Paperback. Condition: New. 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 the E2E graph learning pipeline in a world of dynamic and evolving graphs.Understand the importance of graph learning for boosting enterprise-grade applicationsNavigate the challenges surrounding the development and deployment of enterprise-ready graph learning and inference pipelinesUse traditional and advanced graph learning techniques to tackle graph use casesUse and contribute to PyGraf, an open source graph learning library, to help embed best practices while building graph applicationsDesign and implement a graph learning algorithm using publicly available and syntactic dataApply privacy-preserved techniques to the graph learning process.

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Paperback. Condition: New. 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 the E2E graph learning pipeline in a world of dynamic and evolving graphs.Understand the importance of graph learning for boosting enterprise-grade applicationsNavigate the challenges surrounding the development and deployment of enterprise-ready graph learning and inference pipelinesUse traditional and advanced graph learning techniques to tackle graph use casesUse and contribute to PyGraf, an open source graph learning library, to help embed best practices while building graph applicationsDesign and implement a graph learning algorithm using publicly available and syntactic dataApply privacy-preserved techniques to the graph learning process.

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Paperback or Softback. Condition: New. Scaling Graph Learning for the Enterprise: Production-Ready Graph Learning and Inference. Book.

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Paperback. Condition: Brand New. 400 pages. 9.19x7.00x9.19 inches. In Stock.

Paperback. Condition: New. 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 the E2E graph learning pipeline in a world of dynamic and evolving graphs.Understand the importance of graph learning for boosting enterprise-grade applicationsNavigate the challenges surrounding the development and deployment of enterprise-ready graph learning and inference pipelinesUse traditional and advanced graph learning techniques to tackle graph use casesUse and contribute to PyGraf, an open source graph learning library, to help embed best practices while building graph applicationsDesign and implement a graph learning algorithm using publicly available and syntactic dataApply privacy-preserved techniques to the graph learning process.

Condition: New.

Paperback. Condition: new. Paperback. 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 the E2E graph learning pipeline in a world of dynamic and evolving graphs.Understand the importance of graph learning for boosting enterprise-grade applicationsNavigate the challenges surrounding the development and deployment of enterprise-ready graph learning and inference pipelinesUse traditional and advanced graph learning techniques to tackle graph use casesUse and contribute to PyGraf, an open source graph learning library, to help embed best practices while building graph applicationsDesign and implement a graph learning algorithm using publicly available and syntactic dataApply privacy-preserved techniques to the graph learning process 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Paperback. Condition: New. 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 the E2E graph learning pipeline in a world of dynamic and evolving graphs.Understand the importance of graph learning for boosting enterprise-grade applicationsNavigate the challenges surrounding the development and deployment of enterprise-ready graph learning and inference pipelinesUse traditional and advanced graph learning techniques to tackle graph use casesUse and contribute to PyGraf, an open source graph learning library, to help embed best practices while building graph applicationsDesign and implement a graph learning algorithm using publicly available and syntactic dataApply privacy-preserved techniques to the graph learning process.

Taschenbuch. Condition: Neu. Neuware - 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.

Taschenbuch. Condition: Neu. 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.

Paperback. Condition: new. Paperback. 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 the E2E graph learning pipeline in a world of dynamic and evolving graphs.Understand the importance of graph learning for boosting enterprise-grade applicationsNavigate the challenges surrounding the development and deployment of enterprise-ready graph learning and inference pipelinesUse traditional and advanced graph learning techniques to tackle graph use casesUse and contribute to PyGraf, an open source graph learning library, to help embed best practices while building graph applicationsDesign and implement a graph learning algorithm using publicly available and syntactic dataApply privacy-preserved techniques to the graph learning process 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: Brand New. 400 pages. 9.19x7.00x9.19 inches. In Stock. This item is printed on demand.

Paperback. Condition: new. Paperback. 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 the E2E graph learning pipeline in a world of dynamic and evolving graphs.Understand the importance of graph learning for boosting enterprise-grade applicationsNavigate the challenges surrounding the development and deployment of enterprise-ready graph learning and inference pipelinesUse traditional and advanced graph learning techniques to tackle graph use casesUse and contribute to PyGraf, an open source graph learning library, to help embed best practices while building graph applicationsDesign and implement a graph learning algorithm using publicly available and syntactic dataApply privacy-preserved techniques to the graph learning process 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.