Graph is The ML Game Changer
With native support for relationships, graph databases provide a holistic view of interconnected data, empowering ML models to make accurate predictions and gain valuable insights.
"ArangoDB features deep machine learning and analytics. There are a lot of ML functions built in that you can easily combine in your queries."
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with Graph today
(no credit card required), and experience the shortest time to value for a hosted graph DB.
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Case Studies
Learn why companies across industries are switching to ArangoDB for Graph.
suited for ML
Native Support For Relationships
Graph databases are well-suited for Machine Learning (ML) due to their native support for relationships (unlike SQL databases). Context and relationships matter: While traditional ML is centered around individual entities, Graph ML consider entities in context and hence enables better and faster results.
straightforward
Simplified
Data Prep
With graph databases, data preparation for ML becomes more straightforward. The interconnected nature of the data is already well-represented, reducing the need for data restructuring and preparation steps often required in relational databases.
solve problems
Applied
Graph ML
With applied Graph ML, you can solve actual business problems and give you usable building blocks. For example, node similarity, classification, etc. for fraud detection, recommendations, etc.
Graph databases' efficient representation of relationships enables real-time data analysis and exploration. ML models can access and analyze interconnected data quickly, allowing for faster feedback and insights.
scaling ML
Scalability &
Performance
ML models often require vast amounts of data to be trained effectively. Graph databases are designed to handle large-scale datasets efficiently. As the volume of data grows, graph databases can scale horizontally by adding more machines to the cluster. Parallel processing is another key capability to scale and delivery sub-second query response.
enhancing ml
More Accurate &
Insightful Predictions
Accuracy for ML models is enhanced in graph databases due to their native support for relationships and the efficient representation of interconnected data. In graph databases, data is stored in a graph-like structure of vertices (nodes) and edges, where vertices represent entities, and edges represent the relationships between these entities.
Get started
with Graph today
(no credit card required), and experience the shortest time to value for a hosted graph DB.
Read the
Case Studies
Learn why companies across industries are switching to ArangoDB for Graph.