Abstract: Systems and methods are provided for servicing inference request by one of multiple machine learning models attached to a deployment cluster. The API server of a deployment cluster is not tightly coupled to any of multiple machine learning models attached to the deployment cluster. Upon receiving an inference request, the deployment cluster can retrieve the configuration parameters, including serialization formatting, for a target model identified in the inference request. The deployment cluster can utilize the retrieved parameters to service the inference request and return the results to a business system application.

Abstract: A system and method for evaluating performance of a model used in providing a response to a product help inquiry includes receiving the product help inquiry, classifying the product help inquiry as being associated with a topic related to a product, and retrieving a path of actions provided in a help documentation associated with the topic. A prompt is also generated based on the product help inquiry for transmission to the model and a response is provided by the model, before a path of actions included the response is extracted. Contextual embeddings for the extracted path are generated and semantic similarities between contextual embeddings for the extracted path and embeddings generated for an expected response are measured. By generating contextual embeddings for the extracted path instead of the entire response, resources required for evaluating the response are significantly reduced. A path coverage metric is measured for the extracted path.

Abstract: A computer implemented method includes accessing instructional content that describes a task for completion by a user. Actions described in the instructional content are derived from the instructional content. Telemetry containing logged actions taken by users is accessed and used to identify actions taken that are associated with the task. A machine learning model is used to identify a task completion path endpoint for the instructional content based on the derived actions and actions taken associated with the task.

Abstract: Systems and methods are provided for servicing inference request by one of multiple machine learning models attached to a deployment cluster. The API server of a deployment cluster is not tightly coupled to any of multiple machine learning models attached to the deployment cluster. Upon receiving an inference request, the deployment cluster can retrieve the configuration parameters, including serialization formatting, for a target model identified in the inference request. The deployment cluster can utilize the retrieved parameters to service the inference request and return the results to a business system application.

Abstract: Systems and methods are provided for remote submission and execution of machine learning models. Embodiments in accordance with the present disclosure enable an instance of a notebook client running on a user terminal to remotely submit model code entered into the cells of the instance to a selected training cluster. The instance is instantiated without configuring the instance with a specific compute engine. A management system communicatively coupled to the user terminal and the training clusters maintains a data structure including configuration parameters for the training clusters. The instance receives a selection of a training cluster and is provided with the configuration parameters from the management system for the selected training cluster for attaching the training cluster to the instance.

Abstract: Described herein are systems, methods, and software to migrate virtual nodes of a data processing cluster. In one implementation, a management system monitors an executing data processing cluster on one or more first hosts to determine when the data processing cluster satisfies migration criteria. Once satisfied, the management system selects one or more second hosts to support the data processing cluster based on accommodation data associated with the hosts. After selection, the management system may initiate operations to migrate the data processing cluster from the one or more first hosts to the one or more second hosts.

Abstract: Systems and methods are provided for servicing inference request by one of multiple machine learning models attached to a deployment cluster. The API server of a deployment cluster is not tightly coupled to any of multiple machine learning models attached to the deployment cluster. Upon receiving an inference request, the deployment cluster can retrieve the configuration parameters, including serialization formatting, for a target model identified in the inference request. The deployment cluster can utilize the retrieved parameters to service the inference request and return the results to a business system application.

Abstract: Systems and methods are provided for servicing inference request by one of multiple machine learning models attached to a deployment cluster. The API server of a deployment cluster is not tightly coupled to any of multiple machine learning models attached to the deployment cluster. Upon receiving an inference request, the deployment cluster can retrieve the configuration parameters, including serialization formatting, for a target model identified in the inference request. The deployment cluster can utilize the retrieved parameters to service the inference request and return the results to a business system application.

Abstract: Systems and methods are provided for remote submission and execution of machine learning models. Embodiments in accordance with the present disclosure enable an instance of a notebook client running on a user terminal to remotely submit model code entered into the cells of the instance to a selected training cluster. The instance is instantiated without configuring the instance with a specific compute engine. A management system communicatively coupled to the user terminal and the training clusters maintains a data structure including configuration parameters for the training clusters. The instance receives a selection of a training cluster and is provided with the configuration parameters from the management system for the selected training cluster for attaching the training cluster to the instance.

Abstract: Systems and methods are provided for servicing inference request by one of multiple machine learning models attached to a deployment cluster. The API server of a deployment cluster is not tightly coupled to any of multiple machine learning models attached to the deployment cluster. Upon receiving an inference request, the deployment cluster can retrieve the configuration parameters, including serialization formatting, for a target model identified in the inference request. The deployment cluster can utilize the retrieved parameters to service the inference request and return the results to a business system application.

Abstract: Described herein are systems, methods, and software to enhance the management of virtual nodes in a computing environment. In one example, a method includes identifying a transition event for a first version of a virtual node executing on a first host, identifying a second host to support a second version of the virtual node, and initiating execution of the second version of the virtual node on the second host. In some implementations, the virtual node comprises a non-persistent portion on the executing host and a persistent portion on a second storage element.

Abstract: Described herein are systems, methods, and software to enhance the management of virtual nodes in a computing environment. In one example, a method includes identifying a transition event for a first version of a virtual node executing on a first host, identifying a second host to support a second version of the virtual node, and initiating execution of the second version of the virtual node on the second host. In some implementations, the virtual node comprises a non-persistent portion on the executing host and a persistent portion on a second storage element.

Abstract: Described herein are systems, methods, and software to migrate virtual nodes of a data processing cluster. In one implementation, a management system monitors an executing data processing cluster on one or more first hosts to determine when the data processing cluster satisfies migration criteria. Once satisfied, the management system selects one or more second hosts to support the data processing cluster based on accommodation data associated with the hosts. After selection, the management system may initiate operations to migrate the data processing cluster from the one or more first hosts to the one or more second hosts.

Abstract: Described herein are systems, methods, and software to enhance the management of virtual nodes in a computing environment. In one example, a method includes identifying a transition event for a first version of a virtual node executing on a first host, identifying a second host to support a second version of the virtual node, and initiating execution of the second version of the virtual node on the second host. In some implementations, the virtual node comprises a non-persistent portion on the executing host and a persistent portion on a second storage element.

Abstract: Described herein are systems, methods, and software to enhance the management of virtual nodes in a computing environment. In one example, a method includes identifying a transition event for a first version of a virtual node executing on a first host, identifying a second host to support a second version of the virtual node, and initiating execution of the second version of the virtual node on the second host. In some implementations, the virtual node comprises a non-persistent portion on the executing host and a persistent portion on a second storage element.