The deep dive debates the question of how the somehow elitist “playing around” of data scientists with machine learning models becomes a productive and stable application for the everyday business. Specifically, using three real-life case studies from the speakers consulting experience, various approaches and technical components are shown that enable the deployment of ML models: First, you will learn how a sales forecasting model of a delivery service created in KNIME was implemented with KNIME server. Secondly, a credit scoring model created in R becomes productive in a Databricks / Azure cloud environment. How would that have looked alternatively with the Cloudera Data Science Workbench in an on-premises Hadoop environment? Finally it is shown how a fraud detection model in Python was deployed as a web service using open-source components (Flask, Kubernetes, Dockers). The pros and cons and the hidden pitfalls are outlined beyond the colorful presentations of software vendors.