Abstract: A determination is made as to whether a value of a first parameter of a first application is to be obtained using a natural language interaction. Based on received input, a first service of a plurality of services is identified. The first service is to be used to perform a first task associated with the first parameter. Portions of the first application to determine the value of the first parameter and to invoke the first service are generated.

Abstract: Methods and apparatus for providing persistent execution environments for computation systems including but not limited to interactive computation systems. A service is provided that extends the notion of static containers to dynamically changing execution environments into which users can install code, add files, etc. The execution environments are monitored, and changes to an execution environment are automatically persisted to environment versions(s) so that code run in the execution environment can be run later or elsewhere simply by referring to the environment. There is no explicit build step for the user. Instead, incremental changes are added to environment versions which are stored and are ready to be used to instantiate respective execution environments on other compute instances.

Abstract: Techniques for automated machine learning (ML) pipeline exploration and deployment are described. An automated ML pipeline generation system allows users to easily construct optimized ML pipelines by providing a dataset, identifying a target column in the dataset, and providing an exploration budget. Multiple candidate ML pipelines can be identified and evaluated through an exploration process, and a best ML pipeline can be provided to the requesting user or deployed for production inference. Users can configure, monitor, and adapt the exploration at multiple points in time throughout.

Abstract: Methods, systems, and computer-readable media for debugging and profiling of machine learning model training are disclosed. A machine learning analysis system receives data associated with training of a machine learning model. The data was collected by a machine learning training cluster. The machine learning analysis system performs analysis of the data associated with the training of the machine learning model. The machine learning analysis system detects one or more conditions associated with the training of the machine learning model based at least in part on the analysis. The machine learning analysis system generates one or more alarms describing the one or more conditions associated with the training of the machine learning model.

Abstract: Methods and apparatus for providing persistent execution environments for computation systems including but not limited to interactive computation systems. A service is provided that extends the notion of static containers to dynamically changing execution environments into which users can install code, add files, etc. The execution environments are monitored, and changes to an execution environment are automatically persisted to environment versions(s) so that code run in the execution environment can be run later or elsewhere simply by referring to the environment. There is no explicit build step for the user. Instead, incremental changes are added to environment versions which are stored and are ready to be used to instantiate respective execution environments on other compute instances.

Abstract: Artifacts, including parameters are data sets, associated with experiment tasks are stored at an experiment management service. A query specifying a particular value of a parameter and a particular data set is received, and an indication of an experiment result associated with the particular data set and the particular parameter value is provided.

Abstract: Techniques for automated machine learning (ML) pipeline exploration and deployment are described. An automated ML pipeline generation system allows users to easily construct optimized ML pipelines by providing a dataset, identifying a target column in the dataset, and providing an exploration budget. Multiple candidate ML pipelines can be identified and evaluated through an exploration process, and a best ML pipeline can be provided to the requesting user or deployed for production inference. Users can configure, monitor, and adapt the exploration at multiple points in time throughout.

Abstract: Artifacts, including parameters are data sets, associated with experiment tasks are stored at an experiment management service. A query specifying a particular value of a parameter and a particular data set is received, and an indication of an experiment result associated with the particular data set and the particular parameter value is provided.

Abstract: Methods, systems, and computer-readable media for automated problem detection for machine learning models are disclosed. A machine learning analysis system receives data associated with use of a machine learning model. The data was collected by a machine learning inference system and comprises input to the model or a plurality of inferences representing output of the machine learning model. The machine learning analysis system performs analysis of the data associated with the use of the machine learning model. The machine learning analysis system detects one or more problems associated with the use of the machine learning model based at least in part on the analysis. The machine learning analysis system initiates one or more remedial actions associated with the one or more problems associated with the use of the machine learning model.

Abstract: A determination is made as to whether a value of a first parameter of a first application is to be obtained using a natural language interaction. Based on received input, a first service of a plurality of services is identified. The first service is to be used to perform a first task associated with the first parameter. Portions of the first application to determine the value of the first parameter and to invoke the first service are generated.

Abstract: Methods, systems, and computer-readable media for automated problem detection for machine learning models are disclosed. A machine learning analysis system receives data associated with use of a machine learning model. The data was collected by a machine learning inference system and comprises input to the model or a plurality of inferences representing output of the machine learning model. The machine learning analysis system performs analysis of the data associated with the use of the machine learning model. The machine learning analysis system detects one or more problems associated with the use of the machine learning model based at least in part on the analysis. The machine learning analysis system initiates one or more remedial actions associated with the one or more problems associated with the use of the machine learning model.

Abstract: Techniques for automated machine learning (ML) pipeline exploration and deployment are described. An automated ML pipeline generation system allows users to easily construct optimized ML pipelines by providing a dataset, identifying a target column in the dataset, and providing an exploration budget. Multiple candidate ML pipelines can be identified and evaluated through an exploration process, and a best ML pipeline can be provided to the requesting user or deployed for production inference. Users can configure, monitor, and adapt the exploration at multiple points in time throughout.

Abstract: Methods, systems, and computer-readable media for debugging and profiling of machine learning model training are disclosed. A machine learning analysis system receives data associated with training of a machine learning model. The data was collected by a machine learning training cluster. The machine learning analysis system performs analysis of the data associated with the training of the machine learning model. The machine learning analysis system detects one or more conditions associated with the training of the machine learning model based at least in part on the analysis. The machine learning analysis system generates one or more alarms describing the one or more conditions associated with the training of the machine learning model.

Abstract: A determination is made as to whether a value of a first parameter of a first application is to be obtained using a natural language interaction. Based on received input, a first service of a plurality of services is identified. The first service is to be used to perform a first task associated with the first parameter. Portions of the first application to determine the value of the first parameter and to invoke the first service are generated.

Abstract: Techniques for automated speech recognition (ASR) are described. A user can upload an audio file to a storage location. The user then provides the ASR service with a reference to the audio file. An ASR engine analyzes the audio file, using an acoustic model to divide the audio data into words, and a language model to identify the words spoken in the audio file. The acoustic model can be trained using audio sentence data, enabling the transcription service to accurately transcribe lengthy audio data. The results are punctuated and normalized, and the resulting transcript is returned to the user.

Abstract: Methods, systems, and computer-readable media for exporting dialog-driven applications to digital communication platforms are disclosed. A launch condition is received from a user. The launch condition is caused to be registered with one or more digital communication platforms. Detection of the launch condition is to cause a natural language input to be routed from at least one of the digital communication platforms to an application management service.

Abstract: A natural language understanding model is trained using respective natural language example inputs corresponding to a plurality of applications. A determination is made as to whether a value of a first parameter of a first application is to be obtained using a natural language interaction. Using the natural language understanding model, at least a portion of the first application is generated.

Abstract: Techniques for automated speech recognition (ASR) are described. A user can upload an audio file to a storage location. The user then provides the ASR service with a reference to the audio file. An ASR engine analyzes the audio file, using an acoustic model to divide the audio data into words, and a language model to identify the words spoken in the audio file. The acoustic model can be trained using audio sentence data, enabling the transcription service to accurately transcribe lengthy audio data. The results are punctuated and normalized, and the resulting transcript is returned to the user.

Abstract: Systems, methods, and apparatuses are disclosed for determining lane information of a roadway segment from vehicle probe data. Probe data is received from vehicle camera sensors at a road segment, wherein the probe data includes lane marking data on the road segment. Lane markings are identified, to the extent present, for the left and right boundaries of the lane of travel as well as the adjacent lane boundaries to the left and right of the lane of travel. The identified lane markings are coded, wherein solid lane lines, dashed lane lines, and unidentified or non-existing lane lines are differentiated. The coded lane markings are compiled in a database. A number of lanes are predicted at the road segment from the database of coded lane markings.

Abstract: Methods, systems, and computer-readable media for exporting dialog-driven applications to digital communication platforms are disclosed. A launch condition is received from a user. The launch condition is caused to be registered with one or more digital communication platforms. Detection of the launch condition is to cause a natural language input to be routed from at least one of the digital communication platforms to an application management service.