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: Apparatus include a mode-locked laser cavity configured to produce a mode-locked output beam, wherein the laser cavity includes a gain medium situated in the laser cavity and an intracavity optical coating filter situated in the laser cavity to receive an intracavity beam, wherein the intracavity optical coating filter has an attenuation profile configured to suppress laser oscillation over a selected portion of the gain bandwidth of the gain medium and to increase a bandwidth of the mode-locked output beam based on the suppression. Related optical coatings are disclosed. Methods of arranging coatings and reducing pulse duration are also disclosed.