Abstract: A system comprising a work machine and an attachment removably coupled with the work machine. The attachment includes an identification module comprising a transceiver configured to communicate with a control system of the work machines so as to improve the functionality and efficiency of the attachment and of the work machines.

Abstract: A system comprising a work machine and an attachment removably coupled with the work machine. The attachment includes an identification module comprising a transceiver configured to communicate with a control system of the work machines so as to improve the functionality and efficiency of the attachment and of the work machines.

Abstract: A system comprising a work machine and an attachment removably coupled with the work machine. The attachment includes an identification module comprising a transceiver configured to communicate with a control system of the work machines so as to improve the functionality and efficiency of the attachment and of the work machines.

Abstract: A facility for optimizing machine learning models is described. The facility obtains a description of a machine learning model and a hardware target for the machine learning model. The facility obtains optimization result data from a repository of optimization result data. The facility optimizes the machine learning model for the hardware target based on the optimization result data.

Abstract: A facility for optimizing machine learning models is described. The facility obtains a description of a machine learning model and a hardware target for the machine learning model. The facility obtains optimization result data from a repository of optimization result data. The facility optimizes the machine learning model for the hardware target based on the optimization result data.

Abstract: A facility for optimizing machine learning models is described. The facility obtains a description of a machine learning model and a hardware target for the machine learning model. The facility obtains optimization result data from a repository of optimization result data. The facility optimizes the machine learning model for the hardware target based on the optimization result data.

Abstract: In some aspects, a model of a remote data source is defined. The model includes an entity representing a data segment from the remote data source. The entity has properties, which represent fields of a data structure in the remote data source and have data type descriptors indicating data types used by the fields. The model also includes an attribute specifying a function, which uses the properties as inputs. The entity is matched to exported data from the remote data source, and the exported data is used as input data to the attribute's function.

Abstract: A facility for optimizing machine learning models is described. The facility obtains a description of a machine learning model and a hardware target for the machine learning model. The facility obtains optimization result data from a repository of optimization result data. The facility optimizes the machine learning model for the hardware target based on the optimization result data.

Abstract: A facility for optimizing machine learning models is described. The facility obtains a description of a machine learning model and a hardware target for the machine learning model. The facility obtains optimization result data from a repository of optimization result data. The facility optimizes the machine learning model for the hardware target based on the optimization result data.

Abstract: A facility for optimizing machine learning models is described. The facility obtains a description of a machine learning model and a hardware target for the machine learning model. The facility obtains optimization result data from a repository of optimization result data. The facility optimizes the machine learning model for the hardware target based on the optimization result data.

Abstract: A facility for optimizing machine learning models is described. The facility obtains a description of a machine learning model and a hardware target for the machine learning model. The facility obtains optimization result data from a repository of optimization result data. The facility optimizes the machine learning model for the hardware target based on the optimization result data.

Abstract: A facility for optimizing machine learning models is described. The facility obtains a description of a machine learning model and a hardware target for the machine learning model. The facility obtains optimization result data from a repository of optimization result data. The facility optimizes the machine learning model for the hardware target based on the optimization result data.

Abstract: In some aspects, a model of a remote data source is defined. The model includes an entity representing a data segment from the remote data source. The entity has properties, which represent fields of a data structure in the remote data source and have data type descriptors indicating data types used by the fields. The model also includes an attribute specifying a function, which uses the properties as inputs. The entity is matched to exported data from the remote data source, and the exported data is used as input data to the attribute's function.

Abstract: In some aspects, an interface is provided that has options for defining entities and attributes. A model of a remote data source is defined based on inputs received via the interface. The model includes an entity representing a data segment from the remote data source. The entity has properties, which represent fields of a data structure in the remote data source and have data type descriptors indicating data types used by the fields. The model also includes an attribute specifying a function, which uses the properties as inputs. The entity is matched to exported data from the remote data source. The match is performed based on the exported data elements being arranged in a hierarchy corresponding to the properties and the data elements having the data types corresponding to the data type descriptors. The attribute's function is executed using, as input data, the data elements mapped to the properties.

Abstract: In some aspects, an interface is provided that has options for defining entities and attributes. A model of a remote data source is defined based on inputs received via the interface. The model includes an entity representing a data segment from the remote data source. The entity has properties, which represent fields of a data structure in the remote data source and have data type descriptors indicating data types used by the fields. The model also includes an attribute specifying a function, which uses the properties as inputs. The entity is matched to exported data from the remote data source. The match is performed based on the exported data elements being arranged in a hierarchy corresponding to the properties and the data elements having the data types corresponding to the data type descriptors. The attribute's function is executed using, as input data, the data elements mapped to the properties.