technický dokument

Four ways knowledge graphs accelerate data science for the enterprise

Reduce the bottlenecks that slow enterprise data science, from time-consuming data preparation to the challenge of putting models into production.

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Enterprise data science often slows down in two places: preparing data and operationalizing models. This white paper explores four ways knowledge graphs can help teams reduce the time spent accessing, integrating and cleaning data, while making feature generation, model development and production workflows easier to scale. Download the white paper to see how connected data can help reduce bottlenecks and support a more effective path from data science to deployment.

Explore how knowledge graphs can help reduce data bottlenecks and speed the path to production

  • Learn how knowledge graphs can help reduce the time spent integrating and preparing data from disparate sources
  • See how graph algorithms, automated query generation and profiling can support faster feature engineering
  • Explore how connected data can support model development through integration with machine learning tools and geospatial functions
  • Understand how APIs, data layers and graph-based workflows can help teams operationalize models more effectively in production.

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