Offline learning of adaptive models

For testing or demo purposes, you can train adaptive models offline with historical data or sample customer responses. This method can be useful if you have a historical Data Set or simulated data and you want to perform further analysis, testing, or simulations. For example, when the recording of historical data is active for an adaptive model that runs in your Production environment, you can use this data to train another adaptive model with different settings that you want to test. Testing can help you decide whether you want to deploy an adaptive model with these settings to Production.

Online learning of adaptive models

Adaptive models do not require historical or sample data to learn. At the start of a project, no detailed historical data or Outcomes are available, sometimes referred to as the cold start in machine learning. The Adaptive Decision Manager (ADM) provides optimal handling for cold start scenarios through online learning. The ADM typically starts without any data and quickly updates the models from responses. Even when there is some historical data available, it is not a best practice to train adaptive models offline before you deploy them to Production, because it is difficult to get all the historical input data right, including real-time contextual and interaction history data, and there is always the threat of predictor leakage.

Use case

This tutorial explores the following use case:

A company that serves many customers wants to improve its method for identifying the best proposition for each customer. The company can make several propositions and has already acquired some information about its customers and interaction data in an external database. The Data Scientists at the company are developing and testing new adaptive models with different settings.

The CustomerResponse database column in the following table contains historical responses for a strategy in which each customer receives a group of propositions and can select only one (Insurance1, Insurance2, or Insurance3). When a customer accepts a proposition, the system automatically rejects the other two.

Sample database fragment with customer information and recorded response

The company needs a solution that takes the customer interaction data, feeds the data to adaptive models, and trains the models simultaneously. Adaptive models that are trained with this information can determine with improved accuracy which proposition a customer is most likely to accept.

To support training adaptive models offline for testing or demo purposes, you must create and configure two rules. The first rule is your decision strategy that references the propositions that you want to offer to your customers. This strategy must also reference an adaptive model rule that learns from each recorded customer response. The second rule that you must create is a data flow. The purpose of this data flow is to upload historical or sample data from a dedicated data set into the adaptive model data set.