Model databases track the accuracy of models on pre-trained weights. The models are stored as executable code and extracted on deployment. Instead of extracting runnable code and data out of a database system, we propose higher-order SQL lambda functions for in-database execution. SQL lambda expressions have been introduced to let the user customise otherwise hard-coded data mining operators such as the distance function for k-means clustering. However, database systems parse lambda expressions during the semantic analysis, which does not allow for functions as arguments. This paper proposes higher-order lambda functions that support the execution of functions from a table as input. Higher-order lambda functions expressing machine learning models allow data scientists to monitor the qualities over time and thus eliminate the need for any extraction step. This paper presents the conception of higher-order lambda functions and their embedding into relational algebra using a derived map operator. We further present the current prototype implementation on top of relational database systems and present preliminary results for data mining within SQL.