TensorFlow Evaluator

Supported pipeline types:

  • Data Collector

  • Data Collector Edge
The TensorFlow Evaluator processor uses a TensorFlow machine learning model to generate predictions or classifications of data. For information about supported versions, see Supported Systems and VersionsSupported Systems and Versions in the Data Collector documentation.

Using the TensorFlow Evaluator processor, you can design pipelines that read data and then generate predictions or classifications of the data during the pipeline processing - producing data-driven insights in real time. For example, you can design pipelines that detect fraudulent transactions or that perform natural language processing as data passes through the pipeline.

To use the TensorFlow Evaluator processor, you first build and train the model in TensorFlow. You then save the trained model to file and store the saved model directory on the Data Collector or Data Collector Edge (SDC Edge) machine that runs the pipeline.

When you configure the TensorFlow Evaluator processor, you define the path to the saved model stored on the Data Collector or SDC Edge machine. You also define the input and output tensor information as configured during the building and training of the model.

You configure whether the processor evaluates each record or evaluates the entire batch at once. When evaluating the entire batch, the processor writes the prediction or classification results to events.