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: Techniques for implementing a multi-wire protocol and multi-dialect database engine are described. A database engine exposes multiple interfaces in the form of ports that support different database wire protocols. The database engine supports multiple query dialects that can be passed over any one of the supported wire protocols. The database engine can support multiple different query dialects within a single database session.

Abstract: Large amounts of human body movement data may be collected, possibly via streaming data, from one or more sensors worn by a user. The data may be analyzed along with other classification data to generate feedback for the user or for other interested people (e.g., a trainer, a coach, a team member, health professional, etc.). The analysis may utilize one or more machine learning (ML) algorithms that use training data to create one or more ML models. When a user is evaluated after receiving feedback, accuracy of the feedback may be evaluated and fed back to the ML model to continue training the ML model(s).

Abstract: A shared resource service allows multiple clients to agree on rules for accessing a shared resource (e.g., a shared database or a shared service). The shared resource service also allows the clients to make changes to the rules (e.g., via consensus or majority vote). The clients use the shared resource service to enforce the rules, without having to trust each other to follow the rules when accessing the shared resource. First, the clients agree on a set of initial rules for accessing the shared resource and a set of initial rules for making changes to the rules. After the rules are initialized, then the clients can begin using the shared resource in accordance with the agreed upon rules. In response to a request for accessing the shared resource or a request for changing rules, the shared resource service enforce the applicable rules.

Abstract: A geographically distributed system using a hybrid, multi-master design is disclosed. The system may use global tables, for which both reads and writes may be performed locally, but with a single region serving as a master region. All strongly consistent reads and writes may be sent to the master region, whereas eventually consistent reads and writes may be performed in any region with a replica. The master region may not be statically assigned but may move from region to region. The master region may be assigned manually by client preference or may be assigned and updated automatically based on the time of day or workload characteristics.

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: A geographically distributed system using a hybrid, multi-master design is disclosed. The system may use global tables, for which both reads and writes may be performed locally, but with a single region serving as a master region. All strongly consistent reads and writes may be sent to the master region, whereas eventually consistent reads and writes may be performed in any region with a replica. The master region may not be statically assigned but may move from region to region. The master region may be assigned manually by client preference or may be assigned and updated automatically based on the time of day or workload characteristics.

Abstract: The maintaining of a database that is distributed across a front database and one or more cloud databases. Rather than have the front and remote portions of the database be fixed, a data distribution manager evaluates databases segments following a data distribution policy to identify one or more database segments that are to be transferred to or from the one or more cloud databases from or to the front database. In response, the data distribution manager causes the identified database segment to be moved. Accordingly, the data that is in the front database (and the data that is stored in the cloud) may vary over time.

Abstract: Embodiments are directed to declaratively managing software applications, dynamically applying configuration changes to a specified software application based on a service level objective (SLO) and to efficiently performing an impact analysis for a specified service level agreement SLO.

Abstract: Embodiments are directed to declaratively managing software applications, dynamically applying configuration changes to a specified software application based on a service level objective (SLO) and to efficiently performing an impact analysis for a specified service level agreement SLO.

Abstract: Embodiments are directed to declaratively managing software applications, dynamically applying configuration changes to a specified software application based on a service level objective (SLO) and to efficiently performing an impact analysis for a specified service level agreement SLO.

Abstract: Embodiments are directed to declaratively managing software applications, dynamically applying configuration changes to a specified software application based on a service level objective (SLO) and to efficiently performing an impact analysis for a specified service level agreement SLO.