Videos, Slides, Case Studies and other GraphAware related resources

Graph-Powered Machine Learning - Book

10 Jan 2020 publications ML graphs

At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs. Graph-Powered Machine Learning teaches you how to use graph-based algorithms and data organization strategies to develop superior machine learning applications.

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Knowledge Graphs in Action

17 Dec 2019 videos KG

In this presentation, you’ll learn how companies are building Knowledge Graphs with Neo4j and the Hume platform in order to surface previously undiscoverable insights. We’ll go over the process of analysing unstructured data using Machine Learning techniques and how graphs are a wonderful representation for storing Knowledge, making it naturally connectable. Lastly, a Graph Visualisation demonstration will take place, showing new insights discovered from the results of the previous operations.

Social media monitoring with ML-powered Knowledge Graph - Talk

10 Oct 2019 videos ML KG

Are you interested in learning about how machine learning can be leveraged to build a knowledge graph, enabling businesses to differentiate themselves and thrive in today’s competitive marketplace? In this talk, we’ll show you how computer vision, natural language processing and understanding, knowledge enrichment, and graph-native algorithms can be combined to extract valuable insights from various unstructured data sources. Whether you’re a business owner looking to gain a competitive edge or a developer looking to expand your skillset, this talk is sure to be of interest to you.

Social media monitoring with ML-powered Knowledge Graph

10 Oct 2019 slides

Ever wondered how ML can be used to build a Knowledge Graph to allow businesses to successfully differentiate and compete today? We will demonstrate how Computer Vision, NLP/U, knowledge enrichment and graph-native algorithms fit together to build powerful insights from various unstructured data sources.

Fix your microservice architecture using graph analysis - full video

10 Oct 2019 videos microservices graphs analytics

To improve the performance of your microservice architecture, you may consider using graph analysis techniques. By using tools like jQAssistant and Neo4j, you can identify potential issues, better understand the relationships between different services, and even analyze the potential impact of changes on your system. With these tools, you can answer questions like:

Are there any antipatterns present in my microservice architecture? How will certain database refactoring efforts affect the other services in my system? Is my API documentation and specification accurate and up to date? Can I get a clear and current visualization of my entire system?

By implementing graph analysis techniques, you can work towards optimizing the design and functionality of your microservice architecture.