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 requirements 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 tensorflow 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 upload a model docid\ i0 xjvebpvdlgj jhpoin for details create an analytics flow where the function processor is based on your model tensorflow processor for time series data see create an analytics flow docid\ rq bcfhohhir1urhxgirj for details tensorflow images processor for images see tensorflow images processor docid\ dfejfzakgi2rtomt 2 ks for details (optional) add the tengo script processor to modify the model output see tengo script docid\ bpx0mkbzvi vhw43jijyq for details visualize the ml based output in the flows manager docid 5bbsox0doib rc iuliim or through applications docid\ c lhahek2hh3hreyjflrw access analytics models ui to access analytics models log in to manufacturing connect edge from the navigation panel, navigate to analytics > models the models pane appears next steps upload a model docid\ i0 xjvebpvdlgj jhpoin obtain field values from a model docid\ oromx5tsmf76bozswiubw model types docid 05qquubmnsrlqyp boz a