Adobe Experience Platform Sensei Machine Learning API

openapi: 3.0.1
info:
  title: Sensei Machine Learning API
  description: "Sensei Machine Learning API provides a mechanism for data scientists to organize and manage ML services from algorithm onboarding through experimentation and to service deployment.\n\nRelated documentation:\n  * [Data Science Workspace documentation](http://www.adobe.com/go/data-science-overview-en)\n  \nVisualize API calls with Postman (a free, third-party software):\n  * [Sensei Machine Learning API Postman collection on GitHub](https://github.com/adobe/experience-platform-postman-samples/blob/master/apis/experience-platform/Sensei%20Machine%20Learning%20API.postman_collection.json)\n  * [Video guide for creating the Postman environment](https://video.tv.adobe.com/v/28832)\n  * [Steps for importing environments and collections in Postman](https://learning.getpostman.com/docs/postman/collection_runs/using_environments_in_collection_runs/)\n\n\n**API paths**: \n  * PLATFORM Gateway URL: https://<span>platform.adobe.io/ \n  * Base path for this API: /data/sensei\n  * Example of a complete path: https://<span>platform.adobe.io/data/sensei/engines\n\n**Required headers**:\n  * All calls require the headers `Authorization`, `x-gw-ims-org-id`, and `x-api-key`. For more information on how to obtain these values, see the [authentication tutorial](http://www.adobe.com/go/platform-api-authentication-en).\n  * All resources in Experience Platform are isolated to specific virtual sandboxes. All requests to Platform APIs require the header `x-sandbox-name` whose value is the all-lowercase name of the sandbox the operation will take place in (for example, \"prod\"). See the [sandboxes overview](https://adobe.com/go/sandbox-overview-en) for more information.\n  * All requests with a payload in the request body (such as POST, PUT, and PATCH calls) must include the header `Content-Type` with a value of `application/json`. \n  \n**API error handling**: \n  - Refer to the Experience Platform API troubleshooting guide for [FAQs](https://experienceleague.adobe.com/docs/experience-platform/landing/troubleshooting.html#faq), [API status codes](https://experienceleague.adobe.com/docs/experience-platform/landing/troubleshooting.html#api-status-codes), and [request header errors](https://experienceleague.adobe.com/docs/experience-platform/landing/troubleshooting.html#request-header-errors)."
  version: '1.0'
servers:
- url: //{environment}.adobe.io/data/sensei
  variables:
    environment:
      default: platform
      enum:
      - platform
      - platform-stage
tags:
- name: Engines
  description: Engines act as an umbrella entity holding all machine learning instances. This is tied to a Docker image, Java archive, or EGG file which contains machine  learning logic to train and score a Model.
- name: MLInstances
  description: MLInstances represent the combination of a specific Engine with particular set of configurations. An instance is often use-case specific for a customer or client.
- name: MLServices
  description: An MLService is a published trained model that provides your organization with the ability to access and reuse previously developed models. A key feature of MLServices is the ability to automate training and scoring on a scheduled basis.
- name: Experiments
  description: A data scientist conducts multiple experiments to arrive at a high-performing Model while training. This can include changing datasets, features, learning parameters, and hardware.
- name: Models
  description: Allows machine learning Model management and registration (Model artifact that is created by the training process).


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# Full source: https://artifacts.apis.io/adobe/openapi/adobe-aep-sensei-machine-learning-openapi.yml