ML Model Training and Prediction Using Lucidworks Fusion Lucidworks

Running a Fusion ML job runs a Spark job behind the scenes and saves the output model into Fusion blob store. The saved output model can be used later at query or index time. Fusion ML job output shows counters for the number of labels it trained on. When the job is finished, the ML model is accessible from the Fusion blob store. After the model is trained through a Spark job, it can be used in both query and index pipelines. In this example, I will show how to use it in an index pipeline to generate predictions on documents before they are indexed. The index pipeline is configured with an ML stage that is configured to take input field'body_t' and output the prediction into a new field'the_newsgroup_s'. The model id in the stage should be the trained model name.