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Ordered by reported HTTPS, CORS support, no-auth access, then changed/observed facts. Access signals are not a popularity or quality score.
Amazon Kinesis Data Firehose API Reference Amazon Kinesis Data Firehose is a fully managed service that delivers real-time streaming data to destinations such as Amazon Simple Storage Service (Amazon S3), Amazon OpenSearch Service, Amazon Redshift, Splunk, and various other supportd destinations.
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Kinesis Video Streams Signaling Service is a intermediate service that establishes a communication channel for discovering peers, transmitting offers and answers in order to establish peer-to-peer connection in webRTC technology.
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Amazon Kinesis Video Streams: API definition published in the OpenAPI directory for amazonaws.com. See the linked specification for supported operations.
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Amazon Kinesis Video Streams Archived Media: API definition published in the OpenAPI directory for amazonaws.com. See the linked specification for supported operations.
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Amazon Kinesis Video Streams Media: API definition published in the OpenAPI directory for amazonaws.com. See the linked specification for supported operations.
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Amazon Lex Build-Time Actions Amazon Lex is an AWS service for building conversational voice and text interfaces. Use these actions to create, update, and delete conversational bots for new and existing client applications.
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Amazon Lex Model Building V2: API definition published in the OpenAPI directory for amazonaws.com. See the linked specification for supported operations.
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Amazon Lex provides both build and runtime endpoints. Each endpoint provides a set of operations (API). Your conversational bot uses the runtime API to understand user utterances (user input text or voice). For example, suppose a user says "I want pizza", your bot sends this input to Amazon Lex using the runtime API. Amazon Lex recognizes that the user request is for the OrderPizza intent (one of the intents defined in the bot). Then Amazon Lex engages in user conversation on behalf of the bot to elicit required information (slot values, such as pizza size and crust type), and then performs fulfillment activity (that you configured when you created the bot). You use the build-time API to create and manage your Amazon Lex bot. For a list of build-time operations, see the build-time API, .
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This section contains documentation for the Amazon Lex V2 Runtime V2 API operations.
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Amazon Lightsail is the easiest way to get started with Amazon Web Services (Amazon Web Services) for developers who need to build websites or web applications. It includes everything you need to launch your project quickly - instances (virtual private servers), container services, storage buckets, managed databases, SSD-based block storage, static IP addresses, load balancers, content delivery network (CDN) distributions, DNS management of registered domains, and resource snapshots (backups) - for a low, predictable monthly price. You can manage your Lightsail resources using the Lightsail console, Lightsail API, Command Line Interface (CLI), or SDKs. For more information about Lightsail concepts and tasks, see the Amazon Lightsail Developer Guide . This API Reference provides detailed information about the actions, data types, parameters, and errors of the Lightsail service. For more information about the supported Amazon Web Services Regions, endpoints, and service quotas of the Lightsail service, see Amazon Lightsail Endpoints and Quotas in the Amazon Web Services General Reference .
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"Suite of geospatial services including Maps, Places, Routes, Tracking, and Geofencing"
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Amazon Lookout for Equipment is a machine learning service that uses advanced analytics to identify anomalies in machines from sensor data for use in predictive maintenance.
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This is the Amazon Lookout for Metrics API Reference . For an introduction to the service with tutorials for getting started, visit Amazon Lookout for Metrics Developer Guide .
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This is the Amazon Lookout for Vision API Reference. It provides descriptions of actions, data types, common parameters, and common errors. Amazon Lookout for Vision enables you to find visual defects in industrial products, accurately and at scale. It uses computer vision to identify missing components in an industrial product, damage to vehicles or structures, irregularities in production lines, and even minuscule defects in silicon wafers — or any other physical item where quality is important such as a missing capacitor on printed circuit boards.
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Definition of the public APIs exposed by Amazon Machine Learning
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Amazon Macie Classic Amazon Macie Classic has been discontinued and is no longer available. A new Amazon Macie is now available with significant design improvements and additional features, at a lower price and in most Amazon Web Services Regions. We encourage you to take advantage of the new and improved features, and benefit from the reduced cost. To learn about features and pricing for the new Macie, see Amazon Macie . To learn how to use the new Macie, see the Amazon Macie User Guide .
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Amazon Macie
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Amazon Managed Blockchain is a fully managed service for creating and managing blockchain networks using open-source frameworks. Blockchain allows you to build applications where multiple parties can securely and transparently run transactions and share data without the need for a trusted, central authority. Managed Blockchain supports the Hyperledger Fabric and Ethereum open-source frameworks. Because of fundamental differences between the frameworks, some API actions or data types may only apply in the context of one framework and not the other. For example, actions related to Hyperledger Fabric network members such as CreateMember and DeleteMember don't apply to Ethereum. The description for each action indicates the framework or frameworks to which it applies. Data types and properties that apply only in the context of a particular framework are similarly indicated.
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Amazon Mechanical Turk API Reference
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Amazon Mobile Analytics is a service for collecting, visualizing, and understanding app usage data at scale.
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Amazon Neptune Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. The core of Amazon Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with milliseconds latency. Amazon Neptune supports popular graph models Property Graph and W3C's RDF, and their respective query languages Apache TinkerPop Gremlin and SPARQL, allowing you to easily build queries that efficiently navigate highly connected datasets. Neptune powers graph use cases such as recommendation engines, fraud detection, knowledge graphs, drug discovery, and network security. This interface reference for Amazon Neptune contains documentation for a programming or command line interface you can use to manage Amazon Neptune. Note that Amazon Neptune is asynchronous, which means that some interfaces might require techniques such as polling or callback functions to determine when a command has been applied. In this reference, the parameter descriptions indicate whether a command is applied immediately, on the next instance reboot, or
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Amazon Personalize is a machine learning service that makes it easy to add individualized recommendations to customers.
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Amazon Personalize can consume real-time user event data, such as stream or click data, and use it for model training either alone or combined with historical data. For more information see Recording Events .
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Amazon Personalize Runtime: API definition published in the OpenAPI directory for amazonaws.com. See the linked specification for supported operations.
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