Back in January, we previewed Neo4j AuraDS and Google Cloud Vertex AI’s partnership and demonstrated how you can build and deploy graph-based machine learning models. AuraDS is graph data science as a service now running as a managed service on top of GCP.
With the Neo4j Graph Data Science platform, you can easily use graph structure to compute algorithms or create embeddings and increase the accuracy and reliability of machine learning pipelines. The worked example from the blog stores data in AuraDS, and computes graph embeddings with FastRP to feed into the Vertex AI workflow.
If you’re interested in getting started on Google Cloud Vertex AI with AuraDS, this blog is for you.
- Featured Community Member: Gabriel Tardif
- Cheatsheet: Cypher
- Graph Data Science On Google Cloud: Neo4j Aurads And Vertex Ai
- Graphconnect: Keynotes
- Graph Analytics: Determining Important Nodes In A Graph Using Neo4j
- Going Meta: A Series On Graphs, Semantics, And Knowledge
- For Beginners: Working With Graph Databases And Neo4j
- Tweet Of The Week: @rotnroll666
- Of Interest
Read the blog
