Getting started with Real-time Machine Learning (Alpha)


Real-time Machine Learning is not available to all users yet. This feature is in alpha and still being tested. This document is subject to change.

In order to utilize Real-time Machine Learning, you need to have access to an organization provisioned with Adobe Experience Platform and Data Science Workspace. Additionally, you need to have a complete dataset for use in training and scoring.

The guides for Real-time Machine Learning require a working understanding of Python 3, Jupyter Notebooks, data science, and machine learning.

Key Terms:

  • DSL: Domain Specific Language.
  • Edge: Real-time Machine Learning scoring service can be run on Edge clusters closer to your activations and applications.
  • Hub: The current alpha is running the Real-time Machine Learning scoring service on the Adobe Experience Platform Hub while the Experience Edge Network is in development.
  • Node: A Node is the fundamental unit of which graphs are formed. Each node performs a specific task and they can be chained together using links to form a graph that represents an ML pipeline. The task performed by a node represents an operation on input data such as a transformation of data or schema, or a machine learning inference. The node outputs the transformed or inferred value to the next node(s).

Datasets in Adobe Experience Platform

To start using Real-time Machine Learning, you must have access to a dataset. You have the option to use an external dataset and upload it to your JupyterLab environment or create a new dataset within Platform if you have not done so already.


If you already have a dataset you wish to use, you can skip to Next steps.

Use an external dataset

To learn more about using an external dataset such as uploading data to your JupyterLab environment, visit the tutorial on analyzing your data using notebooks.

Create a new dataset

To create a new dataset for use in Real-time Machine Learning, you need a data-schema for your dataset. Next, you need to ingest data using the schema you created. Use the following tutorials to create and populate a dataset for Platform:

Next steps

Once you have prepared your data for Real-time Machine Learning, start by following the Real-time Machine Learning notebook user guide to learn how to create and upload an ONNX model to the Real-time Machine Learning model store.

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