Crate aws_sdk_sagemakera2iruntime

Amazon Augmented AI (Amazon A2I) adds the benefit of human judgment to any machine learning application. When an AI application can’t evaluate data with a high degree of confidence, human reviewers can take over. This human review is called a human review workflow. To create and start a human review workflow, you need three resources: a worker task template, a flow definition, and a human loop.

For information about these resources and prerequisites for using Amazon A2I, see Get Started with Amazon Augmented AI in the Amazon SageMaker Developer Guide.

This API reference includes information about API actions and data types that you can use to interact with Amazon A2I programmatically. Use this guide to:

• Start a human loop with the StartHumanLoop operation when using Amazon A2I with a custom task type. To learn more about the difference between custom and built-in task types, see Use Task Types. To learn how to start a human loop using this API, see Create and Start a Human Loop for a Custom Task Type in the Amazon SageMaker Developer Guide.
• Manage your human loops. You can list all human loops that you have created, describe individual human loops, and stop and delete human loops. To learn more, see Monitor and Manage Your Human Loop in the Amazon SageMaker Developer Guide.

Amazon A2I integrates APIs from various AWS services to create and start human review workflows for those services. To learn how Amazon A2I uses these APIs, see Use APIs in Amazon A2I in the Amazon SageMaker Developer Guide.

Getting Started

Examples are available for many services and operations, check out the examples folder in GitHub.

The SDK provides one crate per AWS service. You must add Tokio as a dependency within your Rust project to execute asynchronous code. To add aws-sdk-sagemakera2iruntime to your project, add the following to your Cargo.toml file:

[dependencies]
aws-config = { version = "1.1.7", features = ["behavior-version-latest"] }
aws-sdk-sagemakera2iruntime = "1.22.0"
tokio = { version = "1", features = ["full"] }

Then in code, a client can be created with the following:

use aws_sdk_sagemakera2iruntime as sagemakera2iruntime;

#[::tokio::main]
async fn main() -> Result<(), sagemakera2iruntime::Error> {
    let config = aws_config::load_from_env().await;
    let client = aws_sdk_sagemakera2iruntime::Client::new(&config);

    // ... make some calls with the client

    Ok(())
}

See the client documentation for information on what calls can be made, and the inputs and outputs for each of those calls.

Using the SDK

Until the SDK is released, we will be adding information about using the SDK to the Developer Guide. Feel free to suggest additional sections for the guide by opening an issue and describing what you are trying to do.

Getting Help

• GitHub discussions - For ideas, RFCs & general questions
• GitHub issues - For bug reports & feature requests
• Generated Docs (latest version)
• Usage examples

Crate Organization

The entry point for most customers will be Client, which exposes one method for each API offered by Amazon Augmented AI Runtime. The return value of each of these methods is a “fluent builder”, where the different inputs for that API are added by builder-style function call chaining, followed by calling send() to get a Future that will result in either a successful output or a SdkError.

Some of these API inputs may be structs or enums to provide more complex structured information. These structs and enums live in types. There are some simpler types for representing data such as date times or binary blobs that live in primitives.

All types required to configure a client via the Config struct live in config.

The operation module has a submodule for every API, and in each submodule is the input, output, and error type for that API, as well as builders to construct each of those.

There is a top-level Error type that encompasses all the errors that the client can return. Any other error type can be converted to this Error type via the From trait.

The other modules within this crate are not required for normal usage.

Modules

• client
  Client for calling Amazon Augmented AI Runtime.
• config
  Configuration for Amazon Augmented AI Runtime.
• error
  Common errors and error handling utilities.
• meta
  Information about this crate.
• operation
  All operations that this crate can perform.
• primitives
  Primitives such as Blob or DateTime used by other types.
• types
  Data structures used by operation inputs/outputs.

Structs

• Client
  Client for Amazon Augmented AI Runtime
• Config
  Configuration for a aws_sdk_sagemakera2iruntime service client.

Enums

• Error
  All possible error types for this service.