Abstract: Systems and methods for managing content delivery functionalities based on machine learning models are provided. In one aspect, content requests are routed in accordance with clusters of historical content requests to optimize cache performance. In another aspect, content delivery strategies for responding to content requests are determined based on a model trained on data related to historical content requests. The model may also be used to determine above-the-fold configurations for rendering responses to content requests. In some embodiments, portions of the model can be executed on client computing devices.

Abstract: A network-based scalable production load test service may be implemented on a provider network including a plurality of computing devices in order to provide load testing for network-based production systems. In some embodiments, the plurality of computing devices is configured to receive a request to capture to a load test data repository items of transaction data for a network-based production service. In some embodiments, the plurality of computing devices is configured to capture to the load test data repository the items of transaction data. The transaction data include input to the network-based production service over a network. In some embodiments, in response to a load test specification received by the scalable production load test service, the plurality of computing devices is configured to dynamically allocate one or more resources to perform a load test of the network-based production service according to the load test specification.

Abstract: Network resources are subject to automated downloading of information by automated-agents that cause business- or user-specific data to be compiled en masse. In order to control resources and information available to automated-agents, markup documents or elements of markup documents are obfuscated such that a client must reconfigure the obfuscated information in order to display the requested content contained in the document. The obfuscation is applied such that the content will render and display to a human user in the same manner as if the element was untransformed; however, the markup will appear syntactically different to an automated-agent. Thus, in response to a user request, a server can transform elements of a structured document and provide the transformed document to the client for reconfiguring and rendering.

Abstract: Service providers may operate one or more services configured to detect requests generated by automated agents. A CAPTCHA may be transmitted in response to requests generated by automated agents. The CAPTCHAs may be included in a modal pop-up box configured to be displayed by a client application displaying a webpage to a customer of the service provider. Furthermore, the CAPTCHAs included in the modal pop-up box may be rendered inactive and caused not to be displayed by client application executing the webpage. Submitted solutions to CAPTCHAs may be presented with a cookie that enables access to resources of the service provider without restriction. Cookies may be tracked and their use may be used to detect automated agent activity.

Abstract: Systems and methods for managing content delivery functionalities based on machine learning models are provided. In one aspect, content requests are routed in accordance with clusters of historical content requests to optimize cache performance. In another aspect, content delivery strategies for responding to content requests are determined based on a model trained on data related to historical content requests. The model may also be used to determine above-the-fold configurations for rendering responses to content requests. In some embodiments, portions of the model can be executed on client computing devices.

Abstract: Systems and methods for managing content delivery functionalities based on machine learning models are provided. In one aspect, content requests are routed in accordance with clusters of historical content requests to optimize cache performance. In another aspect, content delivery strategies for responding to content requests are determined based on a model trained on data related to historical content requests. The model may also be used to determine above-the-fold configurations for rendering responses to content requests. In some embodiments, portions of the model can be executed on client computing devices.

Abstract: Current CAPTCHA tests are designed to be difficult for a bot and simple for a human-user to answer; however, as artificial intelligence improves, bots are more capable of using techniques such as optical character recognition to resolve current CAPTCHAs in similar manners as human-users. By providing a CAPTCHA challenge from a library or set of challenges that are designed in a manner that causes or likely causes a human-user to trivially get the answer to the challenge wrong, helps to confirm that a user is a human-user, as a bot would answer the challenge correctly.

Abstract: Systems and methods for managing content delivery functionalities based on machine learning models are provided. In one aspect, content requests are routed in accordance with clusters of historical content requests to optimize cache performance. In another aspect, content delivery strategies for responding to content requests are determined based on a model trained on data related to historical content requests. The model may also be used to determine above-the-fold configurations for rendering responses to content requests. In some embodiments, portions of the model can be executed on client computing devices.

Abstract: Systems and methods for managing content delivery functionalities based on machine learning models are provided. In one aspect, content requests are routed in accordance with clusters of historical content requests to optimize cache performance. In another aspect, content delivery strategies for responding to content requests are determined based on a model trained on data related to historical content requests. The model may also be used to determine above-the-fold configurations for rendering responses to content requests. In some embodiments, portions of the model can be executed on client computing devices.

Abstract: Online retailers may operate one or more services configured to detect requests generated by automated agents. A CAPTCHA may be transmitted in response to requests generated by automated agents. The CAPTCHAs may be included in a modal pop-up box configured to be displayed by a client application displaying a webpage to a customer of the online retailer. Automated agents receiving the CAPTCHAs may not be blocked or otherwise restricted from the resources requested and therefore may not be configured to interact with the CAPTCHAs contained in the modal pop-up box.

Abstract: A computer-facilitated service receives a request, from a user client, to access a site provided by the service. The service may obtain, from the request, identifying information, which may be used to identify prior activity of the user client. This prior activity is used to determine whether the user client is to be provided with an interstitial user interface component, which may be configured to cause the user client to provide additional information about the client and to be successfully completable by an automated agent or other automated process. If an interstitial user interface component is provided, the service may receive, from the user client, this additional information, which may be used to determine whether the user client is using an automated agent to access the site.

Abstract: Described are techniques for prioritizing devices to receive updates to an application executing on a device. Event data indicative of use of the application, the device, and other information associated with a user is obtained. For example, event data for a user account across many different devices may be obtained. Deployment parameters may specify how an update is to be distributed, such as maximum number of devices to deploy to, timing of deployment, and so forth. Based on the event data and the deployment parameters, a subset of the devices executing the application are selected. The update may then be distributed to the subset.

Abstract: Online retailers may operate one or more services configured to detect requests generated by automated agents. A CAPTCHA may be transmitted in response to requests generated by automated agents. The CAPTCHAs may be included in a modal pop-up box configured to be displayed by a client application displaying a webpage to a customer of the online retailer. Furthermore, the CAPTCHAs included in the modal pop-up box may be rendered inactive and caused not to be displayed by client application executing the webpage. Rendering the CAPTCHAs inactive may provide an additional signal which may be sued to update one or more automated agent detection models.

Abstract: Current CAPTCHA tests are designed to be difficult for a bot and simple for a human-user to answer; however, as artificial intelligence improves, bots are more capable of using techniques such as optical character recognition to resolve current CAPTCHAs in similar manners as human-users. By providing a CAPTCHA challenge from a library or set of challenges that are designed in a manner that causes or likely causes a human-user to trivially get the answer to the challenge wrong, helps to confirm that a user is a human-user, as a bot would answer the challenge correctly.

Abstract: Service providers may operate one or more services configured to detect requests generated by automated agents. A CAPTCHA may be transmitted in response to requests generated by automated agents. The CAPTCHAs may be included in a modal pop-up box configured to be displayed by a client application displaying a webpage to a customer of the service provider. Furthermore, the CAPTCHAs included in the modal pop-up box may be rendered inactive and caused not to be displayed by client application executing the webpage. Submitted solutions to CAPTCHAs may be presented with a cookie that enables access to resources of the service provider without restriction. Cookies may be tracked and their use may be used to detect automated agent activity.

Abstract: Current CAPTCHA tests are designed to be difficult for a bot and simple for a human-user to answer; however, as artificial intelligence improves, bots are more capable of using techniques such as optical character recognition to resolve current CAPTCHAs in similar manners as human-users. By providing a CAPTCHA challenge from a library or set of challenges that are designed in a manner that causes or likely causes a human-user to trivially get the answer to the challenge wrong, helps to confirm that a user is a human-user, as a bot would answer the challenge correctly.

Abstract: Service providers may operate one or more services configured to detect requests generated by automated agents. A CAPTCHA may be transmitted in response to requests generated by automated agents. The CAPTCHAs may be included in a modal pop-up box configured to be displayed by a client application displaying a webpage to a customer of the service provider. Furthermore, the CAPTCHAs included in the modal pop-up box may be rendered inactive and caused not to be displayed by client application executing the webpage. Submitted solutions to CAPTCHAs may be presented with a cookie that enables access to resources of the service provider without restriction. Cookies may be tracked and their use may be used to detect automated agent activity.

Abstract: Functionality is disclosed herein for increasing input data quality across data targets. The mechanism disclosed herein may receive input data, aggregate and organize the input data, and determine an importance score for the organized data. The importance score may abstract both a quantitative measure and periodic measure for creating a sliding scale. The sliding scale may subsequently be used to associate desired alert information concerning data quality events at the data targets.

Abstract: Current CAPTCHA tests are designed to be difficult for a bot and simple for a human-user to answer; however, as artificial intelligence improves, bots are more capable of using techniques such as optical character recognition to resolve current CAPTCHAs in similar manners as human-users. By maintaining a library of security tests and/or questions based on products purchased by the human-user, and using those questions as a CAPTCHA challenge in order to determine if the user is a human-user on how the user responds, CAPTCHA challenges may be directed toward determining a human-user based on responses exhibiting humanity.

Abstract: Online retailers may operate one or more services configured to detect requests generated by automated agents. A CAPTCHA may be transmitted in response to requests generated by automated agents. The CAPTCHAs may be included in a modal pop-up box configured to be displayed by a client application displaying a webpage to a customer of the online retailer. Furthermore, the CAPTCHAs included in the modal pop-up box may be rendered inactive and caused not to be displayed by client application executing the webpage. Rendering the CAPTCHAs inactive may provide an additional signal which may be sued to update one or more automated agent detection models.