Data Science & AI Workbench enables you to query and score models that have been created in Python, R, or another language such as Curl, CLI, Java or Javascript. The model doesn’t have to have been created using Workbench, as long as the model has been deployed as an endpoint. Scoring can be incredibly useful to an organization, including the following “real world” examples:
  • By financial institutions, to determine the level of risk that a loan applicant represents.
  • By debt collectors, to predict the likelihood of a debtor to repay their debt.
  • By marketers, to predict the likelihood of a subscriber list member to respond to a campaign.
  • By retailers, to determine the probability of a customer to purchase a product.
A scoring engine calculates predictions or makes recommendations based on your model. A model’s score is computed based on the model and query operators used:
  • Boolean queries—specify a formula
  • Vector space queries—support free text queries (with no query operators necessarily connecting them)
  • Wildcard queries—match any pattern

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Using an external scoring engine

Advanced scoring techniques used in machine learning algorithms can automatically update models with new data gathered. If you have an external scoring engine that you prefer to use on your models, you can do so within Workbench.