GDS Pipelines and Machine Learning
Discover how to build and manage graph data science pipelines within GDS, integrating with machine learning workflows.
What are GDS Pipelines?
Welcome to GDS Pipelines! In the Neo4j Graph Data Science (GDS) library, a pipeline is a structured workflow for common graph data science tasks.
Think of it as a blueprint that defines a sequence of steps, from feature engineering using graph algorithms to training and deploying machine learning models.
Why Use GDS Pipelines?
GDS Pipelines offer several key benefits:
- Reproducibility: Define your entire workflow once and reuse it.
- Automation: Streamline complex tasks involving multiple graph algorithms and ML steps.
- Operationalization: Easily deploy graph-based machine learning models for continuous prediction.
They help bridge the gap between experimentation and production.
All lessons in this course
- Introduction to GDS Library
- Running GDS Algorithms
- GDS Pipelines and Machine Learning
- Graph Embeddings with GDS