Integrating AI applications into industrial processes and managing software deployment and maintenance is a complex and often costly endeavor. Traditional software development paradigms often fall short when big data becomes a critical factor. In addition, successful implementation requires a high degree of coordination and collaboration between multiple teams. The development of application-specific models for tasks such as prediction, anomaly detection or classification usually requires collaboration between domain experts, data science, data engineering, infrastructure and application development teams.

Consideration of aspects such as ROI, development costs, availability of experts, restrictions on brownfield sites and technical gaps.

Consideration of aspects such as preselection of ML methods, hyperparameter optimization vs. parameter reduction.