Abstract: A dishwasher having a water pump, a water conduit connected to the water pump for distributing water in the dishwasher tub, a door for closing and opening the dishwasher, and a detergent dispenser located on the inside of the door. The detergent dispenser comprises a closeable cavity for receiving a detergent, an outlet into the tub, and a sealed dispenser water inlet configured to be detachably connected to the water conduit when the door is closed. Operation of the pump causes pressurized water to be directed directly to the inside of the detergent dispenser while the detergent dispenser is in a closed state.

Abstract: At a first resource to be used to perform a computing operation, a pair of execution environments is configured. I/O permissions of programs running in the different environments are based on respective sets of constraints. A program performs the operation in one of the environments, with input data being provided to the program from the second environment. A result of the operation is provided to a destination from the second environment.

Abstract: A non-relational database may be emulated using a relational database with a distributed data store. A request to access the non-relational database may be received at a frontend for a relational database engine that emulates an interface for a non-relational database engine. The request may be translated into the format for the relational database engine and performed by the relational database engine. The relational database engine may access data for the non-relational database at storage nodes that store the data in a relational table column of non-relational data type.

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: At a first resource to be used to perform a computing operation, a pair of execution environments is configured. I/O permissions of programs running in the different environments are based on respective sets of constraints. A program performs the operation in one of the environments, with input data being provided to the program from the second environment. A result of the operation is provided to a destination from the second environment.

Abstract: Techniques for using scoring algorithms utilizing containers for flexible machine learning inference are described. In some embodiments, a request to host a machine learning (ML) model within a service provider network on behalf of a user is received, the request identifying an endpoint to perform scoring using the ML model. An endpoint is initialized as a container running on a virtual machine based on a container image and used to score data and return a result of said scoring to a user device.

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: A network-accessible machine learning service is provided herein. For example, the network-accessible machine learning service provider can operate one or more physical computing devices accessible to user devices via a network. These physical computing device(s) can host virtual machine instances that are configured to train machine learning models using training data referenced by a user device. These physical computing device(s) can further host virtual machine instances that are configured to execute trained machine learning models in response to user-provided inputs, generating outputs that are stored and/or transmitted to user devices via the network.

Abstract: Working set ratio estimations of data items in a sliding time window are determined to dynamically allocate storage for the data items. A working set ratio may be determined by accessing a fixed-size array that stores respective timestamps of last accesses of data items to determine which data items are useful to determine an estimate of a working set for the application within a range of time. The working set ratio is then determined from an estimated working set and an amount of computing resources allocated to the application by the estimated working set. The amount of the computing resources allocated to the application may then be automatically scaled according to the determine working set ratio.

Abstract: A request is received associated with a review. Within first content, a first field of interest and a second field of interest are identified and within second content, a third field of interest and a fourth field of interest are identified. A review is generated that includes a first indication of the first field of interest and a second indication of the second field of interest within the first content, as well as a third indication of the third field of interest and a fourth indication of the fourth field of interest within the second content. The review is transmitted to a device of a reviewer for reviewing the content.

Abstract: Methods, systems, and computer-readable media for dynamic prefetching for database queries are disclosed. A query of a database is started according to a first prefetch policy. Before completing the query, the first prefetch policy is changed to a second prefetch policy. A portion of the query is performed according to the second prefetch policy.

Abstract: A request is received associated with reviewing content. As part of the request, one or more conditions are received and the content is analyzed to identify a first field of interest and a second field of interest. The first field of interest and the second field of interest represent fields of interest associated with the review of the content. At least one of the first field of interest or the second field of interest may not satisfy the one or more conditions and the content, or a portion thereof, may be sent for review.

Abstract: A system that implements a scalable data storage service may maintain tables in a data store on behalf of storage service clients. The service may maintain table data in multiple replicas of partitions that are stored on respective computing nodes in the system. In response to detecting an anomaly in the system, detecting a change in data volume on a partition or service request traffic directed to a partition, or receiving a service request from a client to split a partition, the data storage service may create additional copies of a partition replica using a physical copy mechanism. The data storage service may issue a split command defined in an API for the data store to divide the original and additional replicas into multiple replica groups, and to configure each replica group to maintain a respective portion of the table data that was stored in the partition before the split.

Abstract: Distributed database management systems may maintain collections of items spanning multiple partitions. Index structures may correspond to items on one partition or to items on multiple partitions. Item collections and indexes may be replicated. Changes to the data maintained by the distributed database management system may result in updates to multiple index structures. The changes may be compiled into an instruction set applicable to the index structures. In-memory buffers may contain the instructions prior to transmission to affected partitions. Replication logs may be combined with an acknowledgment mechanism for reliable transmission of the instructions to the affected partitions.

Abstract: Methods, systems, and computer-readable media for dynamic prefetching for database queries are disclosed. A query of a database is started according to a first prefetch policy. Before completing the query, the first prefetch policy is changed to a second prefetch policy. A portion of the query is performed according to the second prefetch policy.

Abstract: A network-accessible machine learning service is provided herein. For example, the network-accessible machine learning service provider can operate one or more physical computing devices accessible to user devices via a network. These physical computing device(s) can host virtual machine instances that are configured to train machine learning models using training data referenced by a user device. These physical computing device(s) can further host virtual machine instances that are configured to execute trained machine learning models in response to user-provided inputs, generating outputs that are stored and/or transmitted to user devices via the network.

Abstract: A database system may associate functions with a database table. A request to associate a function with a table in a database system may be received. An association between the table and the function may be created. The function may include parameters that are determined from values within the table which are then invoked by a request to perform the function. The associated function may cause the collection of the values prior to performance of the function.

Abstract: Techniques for batch mode execution for calls to remote services are described. A method of batch mode execution for calls to remote services may include generating, by a query service of a provider network, a query plan to optimize a query for batch processing of data, the query plan including at least a function reference to a function provided by at least one service of the provider network, executing the query plan to invoke the function associated with the function reference, wherein a batch function generates a request including a batch of service calls to be processed by the at least one service, sends the request including the batch of service calls to the at least one service, and obtains a plurality of machine learning responses from the at least one service, and generating a query response based on the plurality of responses.