Industry-specific Machine Learning applications

Nearly all industries and business units can benefit from Machine Learning based applications. In the industrial context, for example, Machine Learning can help predicting when to perform maintenance on machinery, identify anomalies in machine operations, or support engineers to identify the factors which make the difference between a good or bad product batch.

However, there are a few challenges to overcome that should be considered when developing a useful Machine Learning application for productive use.

As shown in the video above, automating the Machine Learning workflow in the background is crucial.

This is how we do it

First, historical data is cleaned and processed so that it can be used for further Machine Learning methods.

This data is used to create an automated Machine Learning workflow on the A1 Digital Machine Learning Platform powered by BigML.

The resulting model (or combination of models as in the example below) is used to automatically generate predictions.

#01 Preprocessing. - #02 Machine Learning. - #03 Predictions.

Example of a Machine Learning workflow with labelled text data as input.


How to involve business analysts

Automating the workflow in the background and developing an easy-to-use application for the business user is important for almost all Machine Learning based applications.

However, to guarantee a stable, high-quality application , it is important to involve the business analyst to monitor the performance of the models in use and who can trigger the automatic retraining / exchange of models whenever needed (e.g. when a model is outdated).



Interested in Smart Business Applications but still have some pending questions? Feel free to contact us any time. Our experts will come back to you as soon as possible.

Stefanie Pichler

Product Manager for Machine Learning and Advanced Data Analytics