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: There is set forth a retractable roof system of a specified arrangement including a first elongated track and a second elongated track, and a beam extending between the first elongated track and the second elongated track. The beam is described as supporting a cover. There is also set forth apparatus for moving the beam in relation to the first elongated track and the second elongated track.

Abstract: A bang-bang servo system controls motors which provide drive for trucks on each end of a fabric support beam structure. A plurality of such truck/beam/fabric units comprise a retractable roof Separation of inner and outer truck guiding tracks/support platforms may be wide and somewhat variable. The radius of curvature of the inner and outer tracks may vary widely over the beam excursion path between open and closed roof conditions. Individual beam motion sequencing and stop position are commanded through a central control unit. These features allow great flexibility and adaptability in application of this invention.