Do you want to use graphs in your machine learning pipelines or analytics dashboards but wonder about feasibility for larger volumes of data? 

Neo4j built graph data science from the ground up to handle the biggest datasets you can come up with. Join this event to learn all the tips and tricks to running graph algorithms quickly and effectively at scale.

At this 60-minute webinar, we will:

  • Present practical approaches to graph machine learning on big datasets 
  • Walk through a hands-on example of feature engineering for a large scale classification problem 
  • Show how Meredith Corp uses graph data science on tens of billions of data points to increase engagement by over 600% 

Learn how to get started with Neo4j best practices for enterprise data volumes across: 

  • Graph-based feature engineering 
  • Model training and prediction 
  • Data ingestion and export patterns 

Don’t miss this session with Neo4 Graph Data Science experts – Dr. Alicia Frame and Zach Blumenfeld – as you kick off your next project!

Dr. Alicia Frame
Director of Product Management, Graph Data Science, Neo4j

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.

Zach Blumenfeld
Data Science Product Specialist, Neo4j

Zach Blumenfeld is a graph enthusiast who helps data scientists, engineers, and business leaders understand and implement Graph Analytics to solve challenging business problems.

He has first hand experience with a wide range of modern day analytical challenges, including criminal fraud detection, identity resolution, and recommendation systems. Serving in both data science and software developer capacities, Zach has applied graph computing for law enforcement and government entities in support of missions that counter drug trafficking, human smuggling, money laundering, and child exploitation. He has led the development and deployment of full stack graph systems designed to facilitate broad search and analytical query requirements.

Zach is excited to have recently joined Neo4j as Data Science Product Specialist, where he will help empower the field with Neo4j’s industry leading Graph Data Science (GDS) capabilities.

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