Enable creating and storing machine-learning models in your application by
specifying a resilient repository for model training and historical data.
CAUTION:
If your application contains complete machine-learning models,
performing this procedure might result in data loss. Proceed only if you are a system
architect.
Before you begin: Create a resilient repository for your machine-learning
models. For more information, see
Integrating with file and content management systems
and
Creating a repository.
If
your application contains complete machine-learning models, minimize the risk of
data loss by saving a copy of the models in your local directory.
For more information, see Exporting text analytics models.
-
In the header of Dev Studio, click .
-
In the Storage section, in the Analytics
repository field, press the Down arrow key, and then select a
repository for the model data.
Warning: Select a resilient repository, for example, an Amazon Web
Services repository. To avoid data loss, do not use the
defaultstore repository that is located under
/tomcat/Work/Catalina/localhost/prweb/.
Result: The model data is stored in the repository that
you
specified, in the
nlpcontents/models folder. For example,
nlpcontents/models/@baseclass/NLPSample/01-01-06/Int_1/trainingdata,
where:
- @baseclass is the class name.
- NLPSample is the ruleset.
- 01-01-06 is the ruleset version.
- Int_1 is the model name.
- trainingdata is the name of the folder that contains
the training data for text analytics models.
- Optional:
To
include training data when you export text analytics models, perform one of the
following actions:
- To migrate text analytics models to production systems, clear the
Include historical data source in text model
export check box.
- To migrate text analytics models to non-production systems, select the
Include historical data source in text model
export check box.
-
In the Confirm repository change dialog box, click
Submit.
-
Click Save.
-
If you saved a copy of the text analytics models in your application as
described in the
Before you begin section, upload the
models to Prediction Studio.