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Amazon Machine Learning

amazonaws · Cloud

Cloud No Auth Free & Open cloud

Definition of the public APIs exposed by Amazon Machine Learning

Authentication

No authentication requiredFree to use with no key needed.

Sample Requests

POST DescribeBatchPredictions

Returns a list of BatchPrediction operations that match the search criteria in the request.

https://api.apis.guru/v2/specs/amazonaws.com/machinelearning/2014-12-12/#X-Amz-Target=AmazonML_20141212.DescribeBatchPredictions?Limit=10&NextToken=example

Hover any highlighted part to learn what it does

Headers — extra info sent with the request
X-Amz-Target AmazonML_20141212.DescribeBatchPredictions 
curl -X POST "https://api.apis.guru/v2/specs/amazonaws.com/machinelearning/2014-12-12/#X-Amz-Target=AmazonML_20141212.DescribeBatchPredictions?Limit=10&NextToken=example" \
  -H "X-Amz-Target: AmazonML_20141212.DescribeBatchPredictions"
import requests
params = {
    "Limit": "10",
    "NextToken": "example"
}
headers = {
    "X-Amz-Target": "AmazonML_20141212.DescribeBatchPredictions"
}
response = requests.post(
    "https://api.apis.guru/v2/specs/amazonaws.com/machinelearning/2014-12-12/#X-Amz-Target=AmazonML_20141212.DescribeBatchPredictions",
    params=params,
    headers=headers,
)
print(response.json())
const url = new URL('https://api.apis.guru/v2/specs/amazonaws.com/machinelearning/2014-12-12/#X-Amz-Target=AmazonML_20141212.DescribeBatchPredictions');
url.searchParams.set('Limit', '10');
url.searchParams.set('NextToken', 'example');

const response = await fetch(url, {
  method: 'POST',
  headers: {
    'X-Amz-Target': 'AmazonML_20141212.DescribeBatchPredictions'
  },
}); 
const data = await response.json();
console.log(data);
package main

import (
	"fmt"
	"io"
	"net/http"
	"net/url"
)

func main() {
	baseURL, _ := url.Parse("https://api.apis.guru/v2/specs/amazonaws.com/machinelearning/2014-12-12/#X-Amz-Target=AmazonML_20141212.DescribeBatchPredictions")
	q := baseURL.Query()
	q.Set("Limit", "10")
	q.Set("NextToken", "example")
	baseURL.RawQuery = q.Encode()
	targetURL := baseURL.String()
	req, _ := http.NewRequest("POST", targetURL, nil)
	req.Header.Set("X-Amz-Target", "AmazonML_20141212.DescribeBatchPredictions")

	client := &http.Client{}
	resp, _ := client.Do(req)
	defer resp.Body.Close()
	body, _ := io.ReadAll(resp.Body)
	fmt.Println(string(body))
}
require "net/http"
require "json"

uri = URI("https://api.apis.guru/v2/specs/amazonaws.com/machinelearning/2014-12-12/#X-Amz-Target=AmazonML_20141212.DescribeBatchPredictions")
uri.query = URI.encode_www_form({
  "Limit" => "10",
  "NextToken" => "example"
})

http = Net::HTTP.new(uri.host, uri.port)
http.use_ssl = uri.scheme == "https"

req = Net::HTTP::Post.new(uri)
req["X-Amz-Target"] = "AmazonML_20141212.DescribeBatchPredictions"

res = http.request(req)
puts JSON.parse(res.body)
<?php
$url = "https://api.apis.guru/v2/specs/amazonaws.com/machinelearning/2014-12-12/#X-Amz-Target=AmazonML_20141212.DescribeBatchPredictions?" . http_build_query([
    "Limit" => "10",
    "NextToken" => "example"
]);
$opts = ["http" => [
    "method" => "POST",
    "header" => implode("\r\n", [
        "X-Amz-Target: AmazonML_20141212.DescribeBatchPredictions"
    ]),
]];
$ctx = stream_context_create($opts);
$res = file_get_contents($url, false, $ctx);
print_r(json_decode($res, true));
POST DescribeDataSources

Returns a list of DataSource that match the search criteria in the request.

https://api.apis.guru/v2/specs/amazonaws.com/machinelearning/2014-12-12/#X-Amz-Target=AmazonML_20141212.DescribeDataSources?Limit=10&NextToken=example

Hover any highlighted part to learn what it does

Headers — extra info sent with the request
X-Amz-Target AmazonML_20141212.DescribeDataSources 
curl -X POST "https://api.apis.guru/v2/specs/amazonaws.com/machinelearning/2014-12-12/#X-Amz-Target=AmazonML_20141212.DescribeDataSources?Limit=10&NextToken=example" \
  -H "X-Amz-Target: AmazonML_20141212.DescribeDataSources"
import requests
params = {
    "Limit": "10",
    "NextToken": "example"
}
headers = {
    "X-Amz-Target": "AmazonML_20141212.DescribeDataSources"
}
response = requests.post(
    "https://api.apis.guru/v2/specs/amazonaws.com/machinelearning/2014-12-12/#X-Amz-Target=AmazonML_20141212.DescribeDataSources",
    params=params,
    headers=headers,
)
print(response.json())
const url = new URL('https://api.apis.guru/v2/specs/amazonaws.com/machinelearning/2014-12-12/#X-Amz-Target=AmazonML_20141212.DescribeDataSources');
url.searchParams.set('Limit', '10');
url.searchParams.set('NextToken', 'example');

const response = await fetch(url, {
  method: 'POST',
  headers: {
    'X-Amz-Target': 'AmazonML_20141212.DescribeDataSources'
  },
}); 
const data = await response.json();
console.log(data);
package main

import (
	"fmt"
	"io"
	"net/http"
	"net/url"
)

func main() {
	baseURL, _ := url.Parse("https://api.apis.guru/v2/specs/amazonaws.com/machinelearning/2014-12-12/#X-Amz-Target=AmazonML_20141212.DescribeDataSources")
	q := baseURL.Query()
	q.Set("Limit", "10")
	q.Set("NextToken", "example")
	baseURL.RawQuery = q.Encode()
	targetURL := baseURL.String()
	req, _ := http.NewRequest("POST", targetURL, nil)
	req.Header.Set("X-Amz-Target", "AmazonML_20141212.DescribeDataSources")

	client := &http.Client{}
	resp, _ := client.Do(req)
	defer resp.Body.Close()
	body, _ := io.ReadAll(resp.Body)
	fmt.Println(string(body))
}
require "net/http"
require "json"

uri = URI("https://api.apis.guru/v2/specs/amazonaws.com/machinelearning/2014-12-12/#X-Amz-Target=AmazonML_20141212.DescribeDataSources")
uri.query = URI.encode_www_form({
  "Limit" => "10",
  "NextToken" => "example"
})

http = Net::HTTP.new(uri.host, uri.port)
http.use_ssl = uri.scheme == "https"

req = Net::HTTP::Post.new(uri)
req["X-Amz-Target"] = "AmazonML_20141212.DescribeDataSources"

res = http.request(req)
puts JSON.parse(res.body)
<?php
$url = "https://api.apis.guru/v2/specs/amazonaws.com/machinelearning/2014-12-12/#X-Amz-Target=AmazonML_20141212.DescribeDataSources?" . http_build_query([
    "Limit" => "10",
    "NextToken" => "example"
]);
$opts = ["http" => [
    "method" => "POST",
    "header" => implode("\r\n", [
        "X-Amz-Target: AmazonML_20141212.DescribeDataSources"
    ]),
]];
$ctx = stream_context_create($opts);
$res = file_get_contents($url, false, $ctx);
print_r(json_decode($res, true));
POST DescribeEvaluations

Returns a list of DescribeEvaluations that match the search criteria in the request.

https://api.apis.guru/v2/specs/amazonaws.com/machinelearning/2014-12-12/#X-Amz-Target=AmazonML_20141212.DescribeEvaluations?Limit=10&NextToken=example

Hover any highlighted part to learn what it does

Headers — extra info sent with the request
X-Amz-Target AmazonML_20141212.DescribeEvaluations 
curl -X POST "https://api.apis.guru/v2/specs/amazonaws.com/machinelearning/2014-12-12/#X-Amz-Target=AmazonML_20141212.DescribeEvaluations?Limit=10&NextToken=example" \
  -H "X-Amz-Target: AmazonML_20141212.DescribeEvaluations"
import requests
params = {
    "Limit": "10",
    "NextToken": "example"
}
headers = {
    "X-Amz-Target": "AmazonML_20141212.DescribeEvaluations"
}
response = requests.post(
    "https://api.apis.guru/v2/specs/amazonaws.com/machinelearning/2014-12-12/#X-Amz-Target=AmazonML_20141212.DescribeEvaluations",
    params=params,
    headers=headers,
)
print(response.json())
const url = new URL('https://api.apis.guru/v2/specs/amazonaws.com/machinelearning/2014-12-12/#X-Amz-Target=AmazonML_20141212.DescribeEvaluations');
url.searchParams.set('Limit', '10');
url.searchParams.set('NextToken', 'example');

const response = await fetch(url, {
  method: 'POST',
  headers: {
    'X-Amz-Target': 'AmazonML_20141212.DescribeEvaluations'
  },
}); 
const data = await response.json();
console.log(data);
package main

import (
	"fmt"
	"io"
	"net/http"
	"net/url"
)

func main() {
	baseURL, _ := url.Parse("https://api.apis.guru/v2/specs/amazonaws.com/machinelearning/2014-12-12/#X-Amz-Target=AmazonML_20141212.DescribeEvaluations")
	q := baseURL.Query()
	q.Set("Limit", "10")
	q.Set("NextToken", "example")
	baseURL.RawQuery = q.Encode()
	targetURL := baseURL.String()
	req, _ := http.NewRequest("POST", targetURL, nil)
	req.Header.Set("X-Amz-Target", "AmazonML_20141212.DescribeEvaluations")

	client := &http.Client{}
	resp, _ := client.Do(req)
	defer resp.Body.Close()
	body, _ := io.ReadAll(resp.Body)
	fmt.Println(string(body))
}
require "net/http"
require "json"

uri = URI("https://api.apis.guru/v2/specs/amazonaws.com/machinelearning/2014-12-12/#X-Amz-Target=AmazonML_20141212.DescribeEvaluations")
uri.query = URI.encode_www_form({
  "Limit" => "10",
  "NextToken" => "example"
})

http = Net::HTTP.new(uri.host, uri.port)
http.use_ssl = uri.scheme == "https"

req = Net::HTTP::Post.new(uri)
req["X-Amz-Target"] = "AmazonML_20141212.DescribeEvaluations"

res = http.request(req)
puts JSON.parse(res.body)
<?php
$url = "https://api.apis.guru/v2/specs/amazonaws.com/machinelearning/2014-12-12/#X-Amz-Target=AmazonML_20141212.DescribeEvaluations?" . http_build_query([
    "Limit" => "10",
    "NextToken" => "example"
]);
$opts = ["http" => [
    "method" => "POST",
    "header" => implode("\r\n", [
        "X-Amz-Target: AmazonML_20141212.DescribeEvaluations"
    ]),
]];
$ctx = stream_context_create($opts);
$res = file_get_contents($url, false, $ctx);
print_r(json_decode($res, true));

Postman Setup Guide

Get Postman ↗
  1. See official documentation for authentication and setup.

What can you build with Amazon Machine Learning?

Amazon Machine Learning is a Cloud API. Developers commonly use cloud APIs for:

  • provisioning and managing cloud infrastructure
  • automating deployments and container orchestration
  • monitoring uptime and performance metrics
  • managing storage buckets and databases
  • setting up auto-scaling and load balancing

No authentication required. This API is open — no signup or key needed. Ideal for quick prototypes and public-facing features. Amazon Machine Learning is free to use, making it a low-risk choice to experiment with.

New to APIs? Read our beginner's guide

Open documentation ↗