Trivial models can be rather common, in some application. For example Anomaly Detection per user or per sensor. The simplest models might basically be computing

Trivial models can be rather common, in some application. For example Anomaly Detection per user or per sensor. The simplest models might basically be computing the number of standard deviations* away the new datapoint is from a typical distribution, and then having a well-chosen threshold on this. Since these models are per entity, if one has 100'000 sensors deployed, then one gets 100'000 such trivial models. Inference might run every 1 hour or so, and consume last 48 hours of data for example. Training might run once per week on the last 4 week. This is to me a good candidate for an ML workload to run directly in a database: - Low compute vs storage ratio for the models. - High number of models. - Often only want a small subset of data as input to model (a few numbers typically). - Relational data highly relevant, for contextual data around the entity. - Simple models with few parameters to store. - Frequent updates to models. - Historical models interesting. To implement checking new models against old, running in parallel Other examples with similar characteristics would be Timeseries Forecasting on many different time-series. Could be sensor data, stock tickers or whatever. * Better to use a robust analog such as Median Absolute Distance.