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: An abstraction engine receives requests identifying a source application and a logical identifier and maps the request attributes to a stored procedure. The stored procedure invokes a native database call with respect to a database and returns the response to the abstraction later, which reformats the response to an object having <key, value> pairs that is independent of the database protocol or organization. Stored procedures may be mapped to various combinations of attributes of requests, which may include attributes such as a user identifier and application version. The stored procedure may return data from a particular database location or perform more complex functions such as filtering functions with using predefined filter criteria and locations or a filtering criteria and/or location derived from the request.

Abstract: Systems, methods, and non-transitory computer-readable storage media for using a distributed ledger to track and manage industrial fixture assets, such as shelves, refrigerators, ovens, and air conditioning units. The distributed ledger is spread among multiple devices and, as devices report new data (such as current and voltage levels), additions to the distributed ledger can be made. The distributed ledger may use blockchain technologies, and each device in the multiple device may have distinct wallets which are used to pay for maintenance requests or other actions.

Abstract: Systems, methods, and non-transitory computer-readable storage media for using a distributed ledger to track and manage information technology fixture assets, such as cash registers, computers, MC40s, and electronic scales. The distributed ledger is spread among multiple devices and, as devices report new data (such as current and voltage levels), additions to the distributed ledger can be made. The distributed ledger may use blockchain technologies, and each device in the multiple device may have distinct wallets which are used to pay for maintenance requests or other actions.

Abstract: An abstraction engine receives requests identifying a source application and a logical identifier and maps the request attributes to a stored procedure. The stored procedure invokes a native database call with respect to a database and returns the response to the abstraction later, which reformats the response to an object having <key, value> pairs that is independent of the database protocol or organization. Stored procedures may be mapped to various combinations of attributes of requests, which may include attributes such as a user identifier and application version. The stored procedure may return data from a particular database location or perform more complex functions such as filtering functions with using predefined filer criteria and locations or a filtering criteria and/or location derived from the request.