We have just released the Neo4j Graph Data Science (GDS) library 1.3, which now leverages Neo4j 4.x, has over 50 algorithms and includes enterprise features like support for role-based access control and multi-database. With the GDS framework, you get a production-ready platform for data science at an enterprise scale.
Join us for this webinar to learn tips and insider tricks to tune your graph data science work for production scale, consistency and efficiency. We’ll share important information and demo features such as:
- Nuanced algorithm parameters like seeding, thresholds and tolerances
- Getting your graph transformed into the analytics workspace
- The estimate function for better memory management
- Role-based access control for fine-grained security
- Exporting graphs for version control
- Experimenting with alpha tier functions
- Implementing your own algorithm with the Pregel API
Alicia Frame is the lead product manager for data science at Neo4j. She's spent the last year translating input from customers, early adopters, and the community into the first truly enterprise product for doing data science with graphs: Neo4j's Graph Data Science Library. She has a phd in computational biology from UNC Chapel Hill, and her background is in data science applications in healthcare and life sciences.
She's worked in academia, government, and the private sector to leverage graph techniques for drug discovery, molecular optimization, and risk assessments -- and is super excited to be making it possible for anyone to use advanced graph techniques with Neo4j.
Amy Hodler is the Graph Analytics & AI program director at Neo4j. She loves seeing how the community uses graph analytics to reveal structures within real-world networks and infer behavior.
Amy is the co-author of the O'Reilly book Applied Graph Algorithms in Apache Spark and Neo4j, published in early 2019 and updated July 2020.
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