Machine Learning Models

the analytics module allows you to execute your machine learning (ml) models “run time” you can feed live data to the model and get live output from it note to work with ml models in analytics, your computer has to meet certain https //www tensorflow\ org/install/gpu if it does not, the ml specific processors will not be available for flow building create a machine learning model for upload to upload a model to analytics, you need a savedmodel format from https //www tensorflow\ org/guide/saved model 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 upload the model see docid\ aygujds ja8pgsyhjs6wz for details create an analytics flow where the function processor is based on your model tensorflow processor for time series data see docid\ kpxn 8iny0fpdr56gcxgy and docid\ hnnbz hkhfedhtoqrhzkq for details tensorflow images processor for images see docid\ kpxn 8iny0fpdr56gcxgy and docid\ cnmz9ih0m9lmr1e3mqpfh for details (optional) add the tengo script processor to modify the model output see docid\ up4gtzk0bdrnunzwg9lwy for details visualize the ml based output in the docid\ ofgzt1vvy ryxjmhk0rsm or through docid\ lx7dl0c5kfauyke7cv9ih access analytics models ui to access analytics models log in to belden horizon data operations from the navigation panel, navigate to analytics > models the models pane appears next steps docid\ aygujds ja8pgsyhjs6wz docid 233uscor6r1tgamgicmv docid\ rxfavkqbqaurojyknunob