Deployment of Machine Learning and Artificial Intelligence Solutions in the Production Environment

How can machine learning models be deployed in the production environment?

Challenge and Motivation

  • AI holds great potential for the optimization of production
  • The use cases here are numerous (e.g. predictive quality)
  • However, many developed models do not make it into production
  • Reasons for this are both the complexity of deploying machine learning models and a lack of structured guidelines (e.g., in the form of components) on how to approach deployment


  • Development of a component-based deployment guideline
  • Realization of the guideline on the basis of concrete use cases from the community


  • Analysis of the deployment requirements and identification of suitable use cases from the community
  • Derivation of the various guideline components such as deployment design, productionizing & testing, monitoring and retraining
  • Validation of the guideline by application to identified use cases