eBook
Scaling Graph Learning for the Enterprise : Production-Ready Graph Learning and Inference
| Τίτλος: | Scaling Graph Learning for the Enterprise : Production-Ready Graph Learning and Inference |
|---|---|
| Περιγραφή: | 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 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-preserving techniques to the graph learning process |
| Συγγραφείς: | Ahmed Menshawy, Sameh Mohamed, Maraim Rizk Masoud |
| Resource Type: | eBook. |
| Θέματα: | Graph theory--Data processing, Graph algorithms |
| Categories: | COMPUTERS / Data Science / Machine Learning, COMPUTERS / Computer Science, COMPUTERS / Software Development & Engineering / General, COMPUTERS / Data Science / Data Modeling & Design, COMPUTERS / Data Science / Data Visualization |
| Βάση Δεδομένων: | eBook Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edsebk DbLabel: eBook Index An: 4288162 RelevancyScore: 981 AccessLevel: 6 PubType: eBook PubTypeId: ebook PreciseRelevancyScore: 981.043701171875 |
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| Items | – Name: Title Label: Title Group: Ti Data: Scaling Graph Learning for the Enterprise : Production-Ready Graph Learning and Inference – Name: Abstract Label: Description Group: Ab Data: 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 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-preserving techniques to the graph learning process – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ahmed+Menshawy%22">Ahmed Menshawy</searchLink><br /><searchLink fieldCode="AR" term="%22Sameh+Mohamed%22">Sameh Mohamed</searchLink><br /><searchLink fieldCode="AR" term="%22Maraim+Rizk+Masoud%22">Maraim Rizk Masoud</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Graph+theory--Data+processing%22">Graph theory--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+algorithms%22">Graph algorithms</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Data+Science+%2F+Machine+Learning%22">COMPUTERS / Data Science / Machine Learning</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Computer+Science%22">COMPUTERS / Computer Science</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Software+Development+%26+Engineering+%2F+General%22">COMPUTERS / Software Development & Engineering / General</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Data+Science+%2F+Data+Modeling+%26+Design%22">COMPUTERS / Data Science / Data Modeling & Design</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Data+Science+%2F+Data+Visualization%22">COMPUTERS / Data Science / Data Visualization</searchLink> |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=4288162 |
| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 511.5 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Graph theory--Data processing Type: general – SubjectFull: Graph algorithms Type: general Titles: – TitleFull: Scaling Graph Learning for the Enterprise : Production-Ready Graph Learning and Inference Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ahmed Menshawy – PersonEntity: Name: NameFull: Sameh Mohamed – PersonEntity: Name: NameFull: Maraim Rizk Masoud – PersonEntity: Name: NameFull: Ahmed Menshawy – PersonEntity: Name: NameFull: Sameh Mohamed – PersonEntity: Name: NameFull: Maraim Rizk Masoud IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2025 – D: 14 M: 08 Type: profile Y: 2025 Identifiers: – Type: isbn-print Value: 9781098146061 – Type: isbn-electronic Value: 9781098146030 – Type: isbn-electronic Value: 9781098146023 Titles: – TitleFull: Scaling Graph Learning for the Enterprise : Production-Ready Graph Learning and Inference Type: main |
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