Abstract: A network-based service may provide a machine learning model for different clients. The network-based service may implement an interface that allows a client to identify a test data set for validating versions of the machine learning model specifically for the client. When a new version of the machine learning model is created, a validation test using the test data set identified by the client may be used. Results of the validation test may be used to make a decision regard whether to migrate workloads for the client to the new version of the machine learning model.

Abstract: An indication of training artifacts for a machine learning model to be trained with an input data set having an access restriction is obtained. A representation of a software execution environment containing the artifacts is deployed to a computing platform within an isolated resource group which satisfies the access restriction. A trained version of the machine learning model is generated at the computing platform, and transferred outside the isolated resource group.

Abstract: Objects detected in data may be filtered from an object recognition index. Data for object detection may be received. An object detection technique may be applied to the data to detect an object. If the object does not satisfy indexing criteria for the object recognition index, then the detected object may be excluded from the object recognition index.

Abstract: Objects detected in data may be filtered from an object recognition index. Data for object detection may be received. An object detection technique may be applied to the data to detect an object. If the object does not satisfy indexing criteria for the object recognition index, then the detected object may be excluded from the object recognition index.

Abstract: A synthetic document generator that obtains a configuration for a synthetic document derived from real-world documents. The configuration specifies element templates to be included in the synthetic document and weights for the specified element templates. The system generates synthetic documents based on the configuration; the synthetic documents include diversified versions of the element templates specified in the configuration. Annotation documents are generated for the synthetic documents that include information describing the respective synthetic documents. A machine learning model for analyzing real-world documents can then be trained using the synthetic and annotation documents. Feedback from the analysis of real-world documents by the machine learning model can be used to generate a new configuration for generating additional synthetic and annotation documents which are used to further train the model.

Abstract: Techniques are described for automatically scaling (or “auto scaling”) compute resources—for example, virtual machine (VM) instances, containers, or standalone servers—used to support execution of service-oriented software applications and other types of applications that may process heterogeneous workloads. The resource requirements for a software application can be approximated by measuring “worker pool” utilization of instances of each service, where a worker pool represents a number of requests that the service can process concurrently. A scaling service can thus be configured to scale the compute instances provisioned for a service in proportion to worker pool utilization, that is, compute instances can be added as the fleet's worker pools become more “busy,” while compute instances can be removed when worker pools become inactive.

Abstract: Techniques for analyzing stored video upon a request are described. For example, a method of receiving a first application programming interface (API) request to analyze a stored video, the API request to include a location of the stored video and at least one analysis action to perform on the stored video; accessing the location of the stored video to retrieve the stored video; segmenting the accessed video into chunks; processing each chunk with a chunk processor to perform the at least one analysis action, each chunk processor to utilize at least one machine learning model in performing the at least one analysis action; joining the results of the processing of each chunk to generate a final result; storing the final result; and providing the final result to a requestor in response to a second API request is described.

Abstract: Techniques for analyzing stored video upon a request are described. For example, a method of receiving a first application programming interface (API) request to analyze a stored video, the API request to include a location of the stored video and at least one analysis action to perform on the stored video; accessing the location of the stored video to retrieve the stored video; segmenting the accessed video into chunks; processing each chunk with a chunk processor to perform the at least one analysis action, each chunk processor to utilize at least one machine learning model in performing the at least one analysis action; joining the results of the processing of each chunk to generate a final result; storing the final result; and providing the final result to a requestor in response to a second API request is described.