Andere Kunden interessierten sich auch für
![Anwendungsorientierte Methodik zur frühzeitigen Integration datenbasierter Ansätze in die Anlaufphase disruptiver Produkte]( "Anwendungsorientierte Methodik zur frühzeitigen Integration datenbasierter Ansätze in die Anlaufphase disruptiver Produkte")
Anwendungsorientierte Methodik zur frühzeitigen Integration datenbasierter Ansätze in die Anlaufphase disruptiver Produkte49,00 €![Optimizing Hyperparameters for Machine Learning Algorithms in Production]( "Optimizing Hyperparameters for Machine Learning Algorithms in Production")
Optimizing Hyperparameters for Machine Learning Algorithms in Production39,00 €![Proactive Production Control for a Synchronized Tool Production]( "Proactive Production Control for a Synchronized Tool Production")
Proactive Production Control for a Synchronized Tool Production49,00 €![Grouping of Inspection Characteristics to Reduce Costs of Adaptive Inspection Planning in Variant Production]( "Grouping of Inspection Characteristics to Reduce Costs of Adaptive Inspection Planning in Variant Production")
Grouping of Inspection Characteristics to Reduce Costs of Adaptive Inspection Planning in Variant Production49,00 €![Model-Based Process Prediction for Predictive-Reactive Scheduling in Automated Stem Cell Production]( "Model-Based Process Prediction for Predictive-Reactive Scheduling in Automated Stem Cell Production")
Model-Based Process Prediction for Predictive-Reactive Scheduling in Automated Stem Cell Production39,00 €![Model Predictive Control Strategy to Coordinate Production Planning and Building Automation]( "Model Predictive Control Strategy to Coordinate Production Planning and Building Automation")
Model Predictive Control Strategy to Coordinate Production Planning and Building Automation39,00 €![Modellierung und Prognose der Qualitätswahrnehmung mittels Machine Learning]( "Modellierung und Prognose der Qualitätswahrnehmung mittels Machine Learning")
Modellierung und Prognose der Qualitätswahrnehmung mittels Machine Learning39,00 €
High data quality is the key for performant machine learning (ML) models in produc-tion. In practice, data quality is preprocessed using several DPP methods that are configured into DPP pipelines. The choice of the DPP pipelines poses a major chal-lenge. To guide data scientists, a meta learning-based decision support system (DSS) have been developed, called Meta-DPP, which assists in the selection of suitable DPP pipelines but do not cover production-specific requirements.