Abstract: A technology is described for automating deployment of a machine learning model. An example method may include receiving, via a graphical user interface, credentials for connecting to a data store containing a plurality of datasets and connecting to the data store using the credentials. A selection of a target metric to predict using the machine learning model can be received, via the graphical user interface, and datasets included in the plurality of datasets that correlate to the target metric can be identified by analyzing the datasets to identify an association between the target metric and data contained within the datasets. The datasets can be input to the machine learning model to train the machine learning model to generate predictions of the target metric, and the machine learning model can be deployed to computing resources in a service provider environment to generate predictions associated with the target metric.

Abstract: Systems and method for making popsicles are disclosed. The system for making popsicles, includes: a dewar for storing a cooling agent; and a dipping apparatus to be inserted into the dewar, wherein the dipping apparatus includes: a first end comprising arms to receive a popsicle mold that includes a mixture of ingredients and/or liquids, a second end comprising an attachment portion configured to attach to an opening in the dewar, and a holder portion oriented between the first end and the second end of the dipping apparatus, the holder portion configured to hold a stick within the mixture in the popsicle mold, wherein the mixture is chilled by the cooling agent when the dipping apparatus is inserted in the dewar to make a popsicle to be held by the stick.