Real-time Machine Learning node reference (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.

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).

The following guide outlines the supported node libraries for Real-time Machine Learning.

Discovering nodes for use in your ML pipeline

Copy the following code into a Python notebook to view all the nodes that are available for use.

from pprint import pprint

from rtml_nodelibs.core.nodefactory import NodeFactory as nf
# Discover Nodes

Example response

{'FieldOps': 'rtml_nodelibs.nodes.standard.preprocessing.fieldops.FieldOps',
 'FieldTrans': 'rtml_nodelibs.nodes.standard.preprocessing.fieldtrans.FieldTrans',
 'ModelUpload': '',
 'ONNXNode': '',
 'Pandas': 'rtml_nodelibs.nodes.standard.preprocessing.pandasnode.Pandas',
 'Processor': 'rtml_nodelibs.nodes.standard.preprocessing.processor.Processor',
 'Receiver': 'rtml_nodelibs.nodes.standard.preprocessing.receiver.Receiver',
 'ScikitLearn': '',
 'Simulator': 'rtml_nodelibs.nodes.standard.preprocessing.simulator.Simulator',
 'Split': 'rtml_nodelibs.nodes.standard.preprocessing.splitter.Split'}

Standard nodes

Standard nodes build upon open source data science libraries such as Pandas and ScikitLearn.


The ModelUpload node is an internal Adobe node that takes a model_path and uploads the model from the local model path to the Real-time Machine Learning blob store.

model = ModelUpload(params={'model_path': model_path})

msg_model = model.process(None, 1)

model_id = msg_model.model['model_id']


ONNXNode is an internal Adobe node that takes a model ID to pull the pre-trained ONNX model and uses it to score on incoming data.


Specify the columns in the same order that you would like the data to be sent to the ONNX model to score.

node_model_score = ONNXNode(params={"features": ['browser', 'device', 'login_page', 'product_page', 'search_page'], "model_id": model_id})


The following Pandas node lets you import any pd.DataFrame method or any general pandas top level function. To learn more about Pandas methods, visit the Pandas methods documentation. For more information on top level functions, visit the Pandas API reference guide for general functions.

The node below uses "import": "map" to import the method name as a string in the parameters, followed by inputting the parameters as a map function. The example below does this by using {"arg": {"Desktop": 1, "Mobile": 0}, "na_action": 0}. After you have the map in place, you have the option to set inplace as True or False. Set inplace as True or False based on whether you want to apply transformation inplace or not. By default "inplace": False creates a new column. Support to provide a new column name is set to be added in a subsequent release. The last line cols can be a single column name or a list of columns. Specify the columns on which you want to apply the transformation. In this example device is specified.

#  df["device"] = df["device"].map({"Desktop":1, "Mobile":0}, na_action=0)

node_device_apply = Pandas(params={"import": "map",
    "kwargs": {"arg": {"Desktop": 1, "Mobile": 0}, "na_action": 0},
    "inplace": True,
    "cols": "device"})

# df["browser"] = df["browser"].map({"chrome": 1, "firefox": 0}, "na_action": 0})

node_browser_apply = Pandas(params={"import": "map",
    "kwargs": {"arg": {"chrome": 1, "firefox": 0}, "na_action": 0},
    "inplace": True,
    "cols": "browser"})


The ScikitLearn node lets you import any ScikitLearn ML model or scaler. Use the table below for more information on any of the values used in the example:

model_train = ScikitLearn(params={
    "features":['browser', 'device', 'login_page', 'product_page', 'search_page'],
    "label": "payment_confirm",
    "mode": "train",
    "model_path": "resources/model.onnx",
    "params": {
        "model": "sklearn.linear_model.LogisticRegression",
        "model_params": {"solver": 'lbfgs'}
msg6 = model_train.process(msg5)
Value Description
features Input features to the model (list of strings).
For example: browser, device, login_page, product_page, search_page
label Target column name (string).
mode Train/test (string).
model_path Path to the save model locally in onnx format.
params.model Absolute import path to the model (string) eg: sklearn.linear_model.LogisticRegression.
params.model_params Model hyperparameters, see the sklearn API (map/dict) documentation for more information.
node_instance.process(data_message_from_previous_node) The method process() takes DataMsg from the previous node and applies transformation. This depends on the current node being used.


Use the following node to split your dataframe into train and test by passing train_size or test_size. This returns a dataframe with a multi-index. You can access train and test dataframes using the following example,“train”).

splitter = Split(params={"train_size": 0.7})
msg5 = splitter.process(msg4)

Next steps

The next step is to create nodes for use in scoring a Real-time Machine Learning model. For more information, visit the Real-time Machine Learning notebook user guide.

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