Neo4j Live Demo: Graph Data Science and Machine Learning
Thinking about incorporating relationships into your data to improve predictions and machine learning models? Maybe you are creating a knowledge graph or looking for a way to improve customer 360, fraud detection, or supply chain performance.
Relationships are highly predictive of behavior, yet most data science models overlook this information because it's difficult to extract from traditional databases. With graphs, relationships are embedded in the data itself, making it practical to add predictive capabilities to your existing practices.
Join our 30-minute demo using Neo4j Graph Data Science, which has 65+ supported, scalable graph algorithms that range from pathfinding and similarity to influencer and community detection. This live demo provides essential resources for predictive modeling and analytics with graph technology.
Our experts will introduce and guide you through these learnings:
- Leveraging graph ML to make breakthrough predictions
- Taking different approaches to graph feature engineering, from queries and algorithms to embeddings
- Fitting Graph Data Science into your data stack with tools and connectors for accessing, storing, moving, and sharing data
You’ll leave this demo with resources for predictive modeling and analytics supplied by Neo4j:
- Algorithms such as community detection, centrality, and similarity
- Integrations for incorporating graph features in your ML workflows
- Graph native ML Pipelines and graph embeddings
- No-code visualization and exploratory data analysis with graph
David Allen is Technology Partner Architect at Neo4j. He is a deeply technical generalist with experience in managing teams and driving towards complex goals. The most fun he has had in his career is when he is learning something new, or trying to figure out how to do something that hasn’t been done before.