Development of machine learning models happens mostly in Jupyter Notebooks these days. To bring them into production, Data scientists struggle sometimes. In this talk, I will show how to transfer your great Jupyter notebook into a docker image that allows you to train your model locally. This local model will also let you predict locally as a service. The nice benefit of this docker image is its scalability: You can, for instance, upload it to AWS Sagemaker and run it on any instance type you want. I will also show you how to implement a prediction endpoint as a callable API.