Abstract: An embodiment is a method performed by one or more electronic device to manage deployment of a configuration object for a machine learning application in a multi-tenant on-demand model serving infrastructure. The method includes receiving an indication that a configuration object for the machine learning application is ready to be deployed, obtaining the configuration object for the machine learning application in response to receiving the indication, and causing a router component to instantiate a router instance for the machine learning application, wherein the router component instantiates the router instance for the machine learning application based on the configuration object for the machine learning application and the router instance for the machine learning application is configured to route incoming scoring requests associated with the machine learning application to a scoring container that provides scoring functionality for the machine learning application.

Abstract: Techniques are disclosed for processing record writes in a database system having a log owner and one or more log tailers. The log owner maintains a transaction log, and the log tailers replay the transaction log to recreate a state of the log owner. A log tailer may receive a write request to perform a record write operation to write a base record to a database as part of a database transaction. The log tailer may determine additional records to write as a result of having to write the base record. The log tailer buffers the base record and additional records in a memory buffer and issues them as a single batch to the log owner to update the transaction log. The log owner may update the transaction log and return a response to the log tailer that indicates whether the log owner successfully updated the transaction log.

Abstract: A computer services environment may include web servers providing access domains and a network ingress paths receiving application-layer request messages. The application-layer request messages may each be received from a respective source via a respective ingress path and may be directed to a domain. The computing services environment may also include an orchestration engine configured to determine and implement mitigation policies corresponding with the ingress paths based on a classification of a subset of the plurality of application-layer request messages as being sent from a subset of the sources associated with a distributed denial of service attack. The mitigation policies may include rules to prevent a subset of subsequent application-layer request messages from the subset of the sources from reaching one or more components of the computing services environment.

Abstract: Systems, devices, and techniques are disclosed for transaction coordination across services. A service including virtual threads receives a transaction that is part of a multi-part transaction. The service determines the transaction does not already exist in transaction state data. The service updates the status of the transaction in the transaction state data to pending. The service processes the transaction. The service updates the status of the transaction in the transaction state data to completed. The service uses receives at and endpoint a request for the status of the transaction. The service responds to the request for the status of the transaction. The service sends a request for status to an endpoint another service that is processing another transaction of the multi-part transaction. The service receives a response from the another service of a status of completed.

Abstract: Embodiments described herein provide A method for training a neural network based model. The methods include receiving a training dataset with a plurality of training samples, and those samples are encoded into representations in feature space. A positive sample is determined from the raining dataset based on a relationship between the given query and the positive sample in feature space. For a given query, a positive sample from the training dataset is selected based on a relationship between the given query and the positive sample in a feature space. One or more negative samples from the training dataset that are within a reconfigurable distance to the positive sample in the feature space are selected, and a loss is computed based on the positive sample and the one or more negative samples. The neural network is trained based on the loss.

Abstract: A computing system may process entity name variations. The entity name variations may be transformed into vector representations. The vector representations may be stored in a database. The entity name variations may be clustered into groups of similar entity name variations based on a similarity metric between the vector representations. A canonical identifier for each group of similar entity name variations may be stored in the database. Each canonical identifier identifying a distinct entity.

Abstract: A computing device displays first data describing a dataset. At least a portion of the first data is encoded with metadata that links the first data to data values and/or data fields of the dataset. The computing device receives a user interaction with a first affordance. The user interaction specifies a first portion of the first data, which includes at least a first data field of the dataset. In response to receiving the user interaction, the computing device retrieves metadata corresponding to the first portion of the first data, and generates second data describing the dataset according to (i) the at least the first data field and (ii) data values of the at least the first data field specified in the metadata, corresponding to the first portion of the first data. The computing device concurrently displays the first data and the second data describing the dataset.

Abstract: A method for data processing is described. The method includes receiving an indication of a reference string from a cloud client of a content generation service. The method further includes generating multiple candidate strings associated with the reference string based on using a machine learning model to calculate similarity metrics between the reference string and the multiple candidate strings, where the machine learning model is trained using a dataset of annotated strings. The method further includes selecting a quantity of the candidate strings based on filtering the multiple candidate strings according to the similarity metrics. The method further includes causing the quantity of candidate strings to be displayed at the cloud client. The method further includes receiving feedback associated with the quantity of candidate strings and a selection of at least one candidate string displayed at the cloud client.

Abstract: Systems, methods, and devices are disclosed herein that provide security for requests sent to services in service meshes. A computing platform may be implemented using a server system. The computing platform is configurable to cause receiving a request from a service in a cloud-based computing environment, and identifying a chain of trust embedded in a portion of the request, the chain of trust being generated by one or more security entities in the cloud-based computing environment, the chain of trust identifying results of one or more security verification operations performed on the request. The computing platform is further configurable to cause determining if the chain of trust is a valid chain of trust based, at least in part, on one or more security policies, and sending the request to another entity in the cloud-based computing environment in response to determining the chain of trust is a valid chain of trust.

Abstract: Database systems and methods are provided for managing relationships between records in a database. An exemplary method involves identifying a virtual container for a first record at the database system based on one or more fields of the first record and updating the virtual container to associate the first record with one or more additional records. In response to an operation with respect to at least one of the records, the method binds an instance variable of a virtual container based on a relationship between respective values associated with the respective records, automatically initiate an action at the database system with respect to the one or more respective records satisfying the relationship constraint, and update a graphical user interface (GUI) display at a client device over a network to include graphical indication of the action at the database system responsive to the bound value for the instance variable.

Abstract: Techniques are described for securing secrets in software build workflows. In some implementations, build instructions call for execution of a first program module and a second program module, where the first program module has been approved to make a privileged request, but the second program module has not. The first program module can be stored in a trusted repository, separately from the second program module. When the first program module is loaded for execution, a cryptographic signature can be validated to determine that the first program module is authentic and as a condition for passing a privileged credential to the first program module. The second program module has no access to the privileged credential. Instead, when the second program module is loaded for execution, a determination can be made whether the second program module makes any privileged requests. Any privileged requests from the second program module will not be fulfilled.

Abstract: Methods, storage systems and computer program products implement embodiments of the present invention for data access that include identifying a set of tables in a database to be accessed by an application, and identifying first and second application programming interface (API) calls having different, respective access properties for accessing records in the tables via an API. Respective numbers of the records in the tables are counted by a processor, and the set of tables are partitioned into first and second subsets responsively to the respective numbers. The records in the first subset of the tables are accessed by having the application convey the first API call to the API, and the records in the second subset of the tables are accessed by having the application convey the second API call to the API.

Abstract: A computer-implemented method is disclosed for predicting a future usage of a cloud-based computing resource based on a previous usage of the resource by users, and predicting an anomaly event at the resource. The method also includes identifying a top contributing user responsible for the anomaly event, throttling an access of the top contributing user, evaluating a speed of data requests received from the top contributing user, and maintaining a utilization level of the resource within a predetermined target range. The method further includes dynamically controlling the speed of data requests based on the evaluation of the speed of data requests and a controlling speed of data request recommended by a first artificial intelligence model. The recommendations of the first artificial intelligence model may be validated by a human reasoning based model configured to monitor and mitigate a risk associated with a counter-intuitive recommendation of the first artificial intelligence model.

Abstract: Techniques are disclosed for managing metadata of a distributed database system in a hybrid manner. A computer system may receive, from a computing device, a request to access a set of data stored in nodes of a distributed storage system that is a caching layer of the system. The system retrieves metadata for a set of data specified in the request, including accessing a reversemap storing a reverse-ordered copy of original metadata stored in a metadata store of the system, where the reversemap is stored on durable storage of the system. Based on retrieving the metadata from the reversemap, the system accesses nodes of the distributed storage system, where the reversemap specifies the nodes of the distributed storage system that store the set of data. The system transmits, to the computing device, information indicating a result of accessing data stored in nodes of the distributed storage system.

Abstract: Techniques are disclosed for managing contention and improving transaction efficiency in distributed database systems through snapshot advancement. A system receives a query defining a first database transaction associated with a first snapshot of a database, where the transaction includes reading and updating a database value. The system evaluates the query against predefined criteria, such as accessing a single database row and deterministically locating the database value, to determine eligibility for snapshot advancement. Upon identifying contention over the database value between the first transaction and a second transaction, the system dynamically advances the transaction to a second snapshot of the database, avoiding a restart. This process involves granting snapshot advancement permission, waiting for a lock held by the second transaction to be released, and using the subsequent snapshot to continue execution.

Abstract: A multi-tenant system receives a request to build a software artifact for deploying on one or more target datacenter entities of the datacenter configured on the could platform. A software artifact comprises executable instructions associated with a service configured for execution on one or more cloud platforms. The system generates a release configuration that includes reusable release components and is used as a template by the release orchestration system to orchestrate release of the software artifact. The release configuration may specify one or more stagger groups, or a group of target datacenter entities that can be rolled-out in parallel.

Abstract: Using container information to select containers for executing models is described. A system receives a request from an application and identifies a version of a machine-learning model associated with the request. The system identifies a set of each serving container corresponding to the machine-learning model from a cluster of available serving containers associated with the version of the machine-learning model. The system selects a serving container from the set of each serving container corresponding to the machine-learning model. If the machine-learning model is not loaded in the serving container, the system loads the machine-learning model in the serving container. If the machine-learning model is loaded in the serving container, the system executes, in the serving container, the machine-learning model on behalf of the request. The system responds to the request based on executing the machine-learning model on behalf of the request.

Abstract: Techniques are disclosed relating to determining whether to utilize an alternative query plan for a subsequent execution of a database query. A computer system accesses a trace record detailing an execution of the database query in accordance with a first query plan selected by a query optimizer. The computer system may determine, based on the trace record, whether the execution of the database query satisfies a set of performance criteria. In response to determining that the execution of the database query does not satisfy the set of performance criteria, the computer system may determine whether one or more alternative query plans for the query include a second query plan that is predicted to be more performant than the first query plan. The computer system may provide an instruction to utilize the second query plan in place of the first query plan for the subsequent execution of the database query.

Abstract: Methods, systems, apparatuses, devices, and computer program products are described. A communication process flow management service may display a set of actions associated with a communication process flow configured to manage communications for a tenant. To update a segment of entities on which the communication process flow is to be executed, a user may navigate to a separate browser tab from that of the communication process flow management service, and may update the segment using an entity segmentation service. The communication process flow management service may receive an indication from an entity segmentation service that a segment of entities is updated and display a notification or a toast popup indicating the segment update to a user interface.