Machine Learning Models
The Analytics module allows you to upload Machine Learning (ML) models, feed live data into the model, and receive live output from the model.
Note: To work with ML models in Analytics, your computer has to meet certain TensorFlow requirements. If it does not, the ML-specific processors will not be available for flow building.
To upload a model to Analytics, you need a SavedModel format from TensorFlow. Once you have created and saved a model, you must compress and zip it prior to uploading it to Analytics. SavedModel is a directory containing serialized signatures and the state needed to run them, including variable values and vocabularies. The SavedModel (saved_model.pb) file stores the TensorFlow model and a set of named signatures, each identifying a function that accepts tensor inputs and produces tensor outputs. SavedModels may contain multiple variants of the model.
Once your ML model is ready, proceed as follows:
- Create an analytics flow based on your model.
- (Optional) Add the Tengo Script processor to modify the model output. See Tengo Script for details.
To access Analytics Models:
- Log in to Litmus Edge.
- From the Navigation panel, navigate to Analytics > Models. The Models pane appears.
