MLnext Execution Homepage

Phoenix Contact GmbH & Co. KG


MLnext Execution is a generic framework to deploy machine learning models into production environments. The focus lies on the continuous processing and evaluation of time series data with machine learning models.

This app uses the MLnext Framework as a free and hardware-independent runtime platform.

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Why should you use MLnext Execution instead of your own machine learning solution?
The use of MLnext Execution offers the following benefits compared to custom programmed algorithms for anomaly detection:

  • Parameterization instead of programming - Through configuration files, the data flow can be flexibly set in the given parameters.
  • Cloud / Edge / Premise execution - The solution can be executed in different cloud applications, on edge or on premise without customization of the algorithms.
  • Isolated execution - Each algorithm gets its own runtime environment, so the processes do not interfere with each other, which ensures stable execution.

The architecture features:

  • a periodic execution service of pre-defined workflows
  • a web interface to monitor and control workflows
  • a prediction endpoint provided as a REST API

A workflow is defined in a three-tier hierarchy. A task defines global namespace for jobs. A job contains a list of steps which are executed in interval. A step is a basic operation such as:

  • data collection from databases (e.g. MySQL, MSSQL, InfluxDB) and MQTT-based services (e.g. Kafka)
  • data preprocessing with scikit-learn pipelines with modules from MLnext, DaskML and scikit-learn
  • processing with ML models written in Tensorflow
  • result evaluation with custom evaluation strategies
  • result saving to databases (e.g. MySQL, MSSQL, InfluxDB) and MQTT-based services (e.g. Kafka)

The MLnext Framework is part of the "Digital Factory now" campaign by Phoenix Contact Electronics to support the solution portfolio "Anomaly Detection".


  • Produktion / Montage
  • Instandhaltung
  • Logistik / Supply Chain Management
  • Kundendienst / Inbetriebnahme


  • Feldebene/Sensoren/Aktoren (field device)
  • Regelung & Steuerung (control device)
  • Station/Maschine oder Maschinengruppe (station)


  • Fertigungs- und Montagevorbereitung
  • Produktionsplanung und –steuerung
  • Teilefertigung
  • Produktionsinstandhaltung

Branche (erprobt)

  • Datenverarbeitungsgeräte, elektronische und optische Erzeugnisse
  • Elektrische Ausrüstungen
  • Maschinenbau

Branche (anwendbar)

  • Keine / Branchenunabhängig


Szenarien / Use cases

  • Anomalieerkennung an Produktionsanlagen
  • Zustandsorientierte Wartung
  • Verschleißbasierte Anomalieerkennung


  • Effizienz - Stillstandzeit/Anzahl und Dauer ungeplanter Produktionsausfälle
  • Effizienz - Taktrate
  • Qualität - Qualitätsmängel
  • Kosten/Nutzen - Instandhaltungsquote (%)


Phoenix Contact GmbH & Co. KG



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