Automated Machine Learning – Was steckt dahinter?

Hello, my name’s Daniel. I’m part of the development team behind the Automated Machine Learning Tool. For me, the highlight of the Machine Learning Tool is how easy it makes the introduction of machine learning models into an industrial environment. The machine experts simply have to load their data into the software as a CSV file, and indicate both the normal performance and the optimal performance of the machine. A model is then trained automatically to be able to distinguish between normal or optimal machine performance and erroneous machine performance. This means that the machine expert is alerted when his machine goes off course. We have data importing, data type identification, feature engineering, data scaling, model selection, model optimisation and model validation. We have automated all of this so that the user only really has to focus on his data, and the tool automatically takes over the entire data science part. Here we rely on Open Source and Python, which make our software extremely future-proof, as it is very easy to integrate model types into the software. Besides anomaly detection, our tool can also perform anomaly classification, so the user can clearly identify the type of anomaly. I’m already delighted with the user feedback. Everything has been so positive. I hope it continues this way.

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