You can import trained Adaptive Decision Manager (ADM) models from your
production environment to a simulation environment. Synchronizing both environments is
useful when you want to run scenarios in your simulation environment and apply the most
up-to-date models. Adaptive models in the production environment are constantly
processing data and self-learning. By importing these models to your simulation
environment, you ensure that the scenarios that you run yield relevant and accurate
- In the source environment, export the pyADMFactory data
This data set is a database table that contains all the adaptive model
instances in your system. For more information, see Exporting data into a data set
- Log on to the target environment and perform the remaining steps there.
- On the Services landing page, on the Adaptive Decision
Manager tab, decommission all ADM nodes by selecting the
appropriate action from the Action menu.
- Open the pyADMFactory data set, and then from the
Actions menu, select
- In the Run Data Set dialog box, from the
Operation list, select
- If the target system has any models that report data in the following tables,
prevent inaccurate reports by manually truncating the following tables:
- Connect to a Cassandra database on a Decision Data Store node.
- Remove any ADM (response) data that may cause a conflict with the source data
by using the following CQL commands:
- drop keyspace adm_commitlog
- drop keyspace null_adm (if present)
- drop keyspace adm (if present)
- Import the pyADMFactory data set from the source
- Recommission all ADM nodes.