Use automated ML in an Azure Machine Learning pipeline in Python

Azure Machine Learning's automated ML capability helps you discover high-performing models without you reimplementing every possible approach. Combined with Azure Machine Learning pipelines, you can create deployable workflows that can quickly discover the algorithm that works best for your data. This article will show you how to efficiently join a data preparation step to an automated ML step. Automated ML can quickly discover the algorithm that works best for your data, while putting you on the road to MLOps and model lifecycle operationalization with pipelines.


Review automated ML's central classes

Automated ML in a pipeline is represented by an AutoMLStep object. The AutoMLStep class is a subclass of PipelineStep. A graph of PipelineStep objects defines a Pipeline.

There are several subclasses of PipelineStep. In addition to the AutoMLStep, this article will show a PythonScriptStep for data preparation and another for registering the model.

The preferred way to initially move data into an ML pipeline is with Dataset objects. To move data between steps and possible save data output from runs, the preferred way is with OutputFileDatasetConfig and OutputTabularDatasetConfig objects. To be used with AutoMLStep, the PipelineData object must be transformed into a PipelineOutputTabularDataset object. For more information, see Input and output data from ML pipelines.

The AutoMLStep is configured via an AutoMLConfig object. AutoMLConfig is a flexible class, as discussed in Configure automated ML experiments in Python.

A Pipeline runs in an Experiment. The pipeline Run has, for each step, a child StepRun. The outputs of the automated ML StepRun are the training metrics and highest-performing model.

To make things concrete, this article creates a simple pipeline for a classification task. The task is predicting Titanic survival, but we won't be discussing the data or task except in passing.

Get started

Retrieve initial dataset

Often, an ML workflow starts with pre-existing baseline data. This is a good scenario for a registered dataset. Datasets are visible across the workspace, support versioning, and can be interactively explored. There are many ways to create and populate a dataset, as discussed in Create Azure Machine Learning datasets. Since we'll be using the Python SDK to create our pipeline, use the SDK to download baseline data and register it with the name 'titanic_ds'.

from azureml.core import Workspace, Dataset

ws = Workspace.from_config()
if not 'titanic_ds' in ws.datasets.keys() :
    # create a TabularDataset from Titanic training data
    web_paths = ['',
    titanic_ds = Dataset.Tabular.from_delimited_files(path=web_paths)

    titanic_ds.register(workspace = ws,
                                     name = 'titanic_ds',
                                     description = 'Titanic baseline data',
                                     create_new_version = True)

titanic_ds = Dataset.get_by_name(ws, 'titanic_ds')

The code first logs in to the Azure Machine Learning workspace defined in config.json (for an explanation, see Create a workspace configuration file. If there isn't already a dataset named 'titanic_ds' registered, then it creates one. The code downloads CSV data from the Web, uses them to instantiate a TabularDataset and then registers the dataset with the workspace. Finally, the function Dataset.get_by_name() assigns the Dataset to titanic_ds.

Configure your storage and compute target

Additional resources that the pipeline will need are storage and, generally, Azure Machine Learning compute resources.

from azureml.core import Datastore
from azureml.core.compute import AmlCompute, ComputeTarget

datastore = ws.get_default_datastore()

compute_name = 'cpu-cluster'
if not compute_name in ws.compute_targets :
    print('creating a new compute target...')
    provisioning_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',
    compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)

        show_output=True, min_node_count=None, timeout_in_minutes=20)

    # Show the result

compute_target = ws.compute_targets[compute_name]


You may choose to use low-priority VMs to run some or all of your workloads. See how to create a low-priority VM.

The intermediate data between the data preparation and the automated ML step can be stored in the workspace's default datastore, so we don't need to do more than call get_default_datastore() on the Workspace object.

After that, the code checks if the AML compute target 'cpu-cluster' already exists. If not, we specify that we want a small CPU-based compute target. If you plan to use automated ML's deep learning features (for instance, text featurization with DNN support) you should choose a compute with strong GPU support, as described in GPU optimized virtual machine sizes.

The code blocks until the target is provisioned and then prints some details of the just-created compute target. Finally, the named compute target is retrieved from the workspace and assigned to compute_target.

Configure the training run

The AutoMLStep configures its dependencies automatically during job submission. The runtime context is set by creating and configuring a RunConfiguration object. Here we set the compute target.

from azureml.core.runconfig import RunConfiguration

aml_run_config = RunConfiguration()
# Use just-specified compute target ("cpu-cluster") = compute_target

Prepare data for automated machine learning

Write the data preparation code

The baseline Titanic dataset consists of mixed numerical and text data, with some values missing. To prepare it for automated machine learning, the data preparation pipeline step will:

  • Fill missing data with either random data or a category corresponding to "Unknown"
  • Transform categorical data to integers
  • Drop columns that we don't intend to use
  • Split the data into training and testing sets
  • Write the transformed data to the OutputFileDatasetConfig output paths
from azureml.core import Run

import pandas as pd 
import numpy as np 
import argparse


def prepare_age(df):
    # Fill in missing Age values from distribution of present Age values 
    mean = df["Age"].mean()
    std = df["Age"].std()
    is_null = df["Age"].isnull().sum()
    # compute enough (== is_null().sum()) random numbers between the mean, std
    rand_age = np.random.randint(mean - std, mean + std, size = is_null)
    # fill NaN values in Age column with random values generated
    age_slice = df["Age"].copy()
    age_slice[np.isnan(age_slice)] = rand_age
    df["Age"] = age_slice
    df["Age"] = df["Age"].astype(int)
    # Quantize age into 5 classes
    df['Age_Group'] = pd.qcut(df['Age'],5, labels=False)
    df.drop(['Age'], axis=1, inplace=True)
    return df

def prepare_fare(df):
    df['Fare'].fillna(0, inplace=True)
    df['Fare_Group'] = pd.qcut(df['Fare'],5,labels=False)
    df.drop(['Fare'], axis=1, inplace=True)
    return df 

def prepare_genders(df):
    genders = {"male": 0, "female": 1, "unknown": 2}
    df['Sex'] = df['Sex'].map(genders)
    df['Sex'].fillna(2, inplace=True)
    df['Sex'] = df['Sex'].astype(int)
    return df

def prepare_embarked(df):
    df['Embarked'].replace('', 'U', inplace=True)
    df['Embarked'].fillna('U', inplace=True)
    ports = {"S": 0, "C": 1, "Q": 2, "U": 3}
    df['Embarked'] = df['Embarked'].map(ports)
    return df
parser = argparse.ArgumentParser()
parser.add_argument('--output_path', dest='output_path', required=True)
args = parser.parse_args()
titanic_ds = Run.get_context().input_datasets['titanic_ds']
df = titanic_ds.to_pandas_dataframe().drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
df = prepare_embarked(prepare_genders(prepare_fare(prepare_age(df))))


print(f"Wrote prepped data to {args.output_path}/prepped_data.csv")

The above code snippet is a complete, but minimal, example of data preparation for the Titanic data. The snippet starts with a Jupyter "magic command" to output the code to a file. If you aren't using a Jupyter notebook, remove that line and create the file manually.

The various prepare_ functions in the above snippet modify the relevant column in the input dataset. These functions work on the data once it has been changed into a Pandas DataFrame object. In each case, missing data is either filled with representative random data or categorical data indicating "Unknown." Text-based categorical data is mapped to integers. No-longer-needed columns are overwritten or dropped.

After the code defines the data preparation functions, the code parses the input argument, which is the path to which we want to write our data. (These values will be determined by OutputFileDatasetConfig objects that will be discussed in the next step.) The code retrieves the registered 'titanic_cs' Dataset, converts it to a Pandas DataFrame, and calls the various data preparation functions.

Since the output_path is a directory, the call to to_csv() specifies the filename prepped_data.csv.

Write the data preparation pipeline step (PythonScriptStep)

The data preparation code described above must be associated with a PythonScripStep object to be used with a pipeline. The path to which the CSV output is written is generated by a OutputFileDatasetConfig object. The resources prepared earlier, such as the ComputeTarget, the RunConfig, and the 'titanic_ds' Dataset are used to complete the specification.

from import OutputFileDatasetConfig
from azureml.pipeline.steps import PythonScriptStep

prepped_data_path = OutputFileDatasetConfig(name="output_path")

dataprep_step = PythonScriptStep(
    arguments=["--output_path", prepped_data_path],

The prepped_data_path object is of type OutputFileDatasetConfig which points to a directory. Notice that it's specified in the arguments parameter. If you review the previous step, you'll see that within the data preparation code, the value of the argument '--output_path' is the directory path at which the CSV file was written.

Train with AutoMLStep

Configuring an automated ML pipeline step is done with the AutoMLConfig class. This flexible class is described in Configure automated ML experiments in Python. Data input and output are the only aspects of configuration that require special attention in an ML pipeline. Input and output for AutoMLConfig in pipelines is discussed in detail below. Beyond data, an advantage of ML pipelines is the ability to use different compute targets for different steps. You might choose to use a more powerful ComputeTarget only for the automated ML process. Doing so is as straightforward as assigning a more powerful RunConfiguration to the AutoMLConfig object's run_configuration parameter.

Send data to AutoMLStep

In an ML pipeline, the input data must be a Dataset object. The highest-performing way is to provide the input data in the form of OutputTabularDatasetConfig objects. You create an object of that type with the read_delimited_files() on a OutputFileDatasetConfig, such as the prepped_data_path, such as the prepped_data_path object.

# type(prepped_data) == OutputTabularDatasetConfig
prepped_data = prepped_data_path.read_delimited_files()

Another option is to use Dataset objects registered in the workspace:

prepped_data = Dataset.get_by_name(ws, 'Data_prepared')

Comparing the two techniques:

Technique Benefits and drawbacks
OutputTabularDatasetConfig Higher performance
Natural route from OutputFileDatasetConfig
Data isn't persisted after pipeline run
Registered Dataset Lower performance
Can be generated in many ways
Data persists and is visible throughout workspace
Notebook showing registered Dataset technique

Specify automated ML outputs

The outputs of the AutoMLStep are the final metric scores of the higher-performing model and that model itself. To use these outputs in further pipeline steps, prepare OutputFileDatasetConfig objects to receive them.

from azureml.pipeline.core import TrainingOutput, PipelineData

metrics_data = PipelineData(name='metrics_data',

model_data = PipelineData(name='best_model_data',

The snippet above creates the two PipelineData objects for the metrics and model output. Each is named, assigned to the default datastore retrieved earlier, and associated with the particular type of TrainingOutput from the AutoMLStep. Because we assign pipeline_output_name on these PipelineData objects, their values will be available not just from the individual pipeline step, but from the pipeline as a whole, as will be discussed below in the section "Examine pipeline results."

Configure and create the automated ML pipeline step

Once the inputs and outputs are defined, it's time to create the AutoMLConfig and AutoMLStep. The details of the configuration will depend on your task, as described in Configure automated ML experiments in Python. For the Titanic survival classification task, the following snippet demonstrates a simple configuration.

from azureml.train.automl import AutoMLConfig
from azureml.pipeline.steps import AutoMLStep

# Change iterations to a reasonable number (50) to get better accuracy
automl_settings = {
    "iteration_timeout_minutes" : 10,
    "iterations" : 2,
    "experiment_timeout_hours" : 0.25,
    "primary_metric" : 'AUC_weighted'

automl_config = AutoMLConfig(task = 'classification',
                             path = '.',
                             debug_log = 'automated_ml_errors.log',
                             compute_target = compute_target,
                             run_configuration = aml_run_config,
                             featurization = 'auto',
                             training_data = prepped_data,
                             label_column_name = 'Survived',

train_step = AutoMLStep(name='AutoML_Classification',

The snippet shows an idiom commonly used with AutoMLConfig. Arguments that are more fluid (hyperparameter-ish) are specified in a separate dictionary while the values less likely to change are specified directly in the AutoMLConfig constructor. In this case, the automl_settings specify a brief run: the run will stop after only 2 iterations or 15 minutes, whichever comes first.

The automl_settings dictionary is passed to the AutoMLConfig constructor as kwargs. The other parameters aren't complex:

  • task is set to classification for this example. Other valid values are regression and forecasting
  • path and debug_log describe the path to the project and a local file to which debug information will be written
  • compute_target is the previously defined compute_target that, in this example, is an inexpensive CPU-based machine. If you're using AutoML's Deep Learning facilities, you would want to change the compute target to be GPU-based
  • featurization is set to auto. More details can be found in the Data Featurization section of the automated ML configuration document
  • label_column_name indicates which column we are interested in predicting
  • training_data is set to the OutputTabularDatasetConfig objects made from the outputs of the data preparation step

The AutoMLStep itself takes the AutoMLConfig and has, as outputs, the PipelineData objects created to hold the metrics and model data.


You must set enable_default_model_output and enable_default_metrics_output to True only if you are using AutoMLStepRun.

In this example, the automated ML process will perform cross-validations on the training_data. You can control the number of cross-validations with the n_cross_validations argument. If you've already split your training data as part of your data preparation steps, you can set validation_data to its own Dataset.

You might occasionally see the use X for data features and y for data labels. This technique is deprecated and you should use training_data for input.

Register the model generated by automated ML

The last step in a simple ML pipeline is registering the created model. By adding the model to the workspace's model registry, it will be available in the portal and can be versioned. To register the model, write another PythonScriptStep that takes the model_data output of the AutoMLStep.

Write the code to register the model

A model is registered in a Workspace. You're probably familiar with using Workspace.from_config() to log on to your workspace on your local machine, but there's another way to get the workspace from within a running ML pipeline. The Run.get_context() retrieves the active Run. This run object provides access to many important objects, including the Workspace used here.

from azureml.core.model import Model, Dataset
from import Run, _OfflineRun
from azureml.core import Workspace
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("--model_name", required=True)
parser.add_argument("--model_path", required=True)
args = parser.parse_args()

print(f"model_name : {args.model_name}")
print(f"model_path: {args.model_path}")

run = Run.get_context()
ws = Workspace.from_config() if type(run) == _OfflineRun else run.experiment.workspace

model = Model.register(workspace=ws,

print("Registered version {0} of model {1}".format(model.version,

Write the PythonScriptStep code

The model-registering PythonScriptStep uses a PipelineParameter for one of its arguments. Pipeline parameters are arguments to pipelines that can be easily set at run-submission time. Once declared, they're passed as normal arguments.

from azureml.pipeline.core.graph import PipelineParameter

# The model name with which to register the trained model in the workspace.
model_name = PipelineParameter("model_name", default_value="TitanicSurvivalInitial")

register_step = PythonScriptStep(script_name="",
                                       arguments=["--model_name", model_name, "--model_path", model_data],

Create and run your automated ML pipeline

Creating and running a pipeline that contains an AutoMLStep is no different than a normal pipeline.

from azureml.pipeline.core import Pipeline
from azureml.core import Experiment

pipeline = Pipeline(ws, [dataprep_step, train_step, register_step])

experiment = Experiment(workspace=ws, name='titanic_automl')

run = experiment.submit(pipeline, show_output=True)

The code above combines the data preparation, automated ML, and model-registering steps into a Pipeline object. It then creates an Experiment object. The Experiment constructor will retrieve the named experiment if it exists or create it if necessary. It submits the Pipeline to the Experiment, creating a Run object that will asynchronously run the pipeline. The wait_for_completion() function blocks until the run completes.

Examine pipeline results

Once the run completes, you can retrieve PipelineData objects that have been assigned a pipeline_output_name. You can download the results and load them for further processing.

metrics_output_port = run.get_pipeline_output('metrics_output')
model_output_port = run.get_pipeline_output('model_output')'.', show_progress=True)'.', show_progress=True)

Downloaded files are written to the subdirectory azureml/{}/. The metrics file is JSON-formatted and can be converted into a Pandas dataframe for examination.

For local processing, you may need to install relevant packages, such as Pandas, Pickle, the AzureML SDK, and so forth. For this example, it's likely that the best model found by automated ML will depend on XGBoost.

!pip install xgboost==0.90
import pandas as pd
import json

metrics_filename = metrics_output._path_on_datastore
# metrics_filename = path to downloaded file
with open(metrics_filename) as f:
   metrics_output_result =
deserialized_metrics_output = json.loads(metrics_output_result)
df = pd.DataFrame(deserialized_metrics_output)

The code snippet above shows the metrics file being loaded from its location on the Azure datastore. You can also load it from the downloaded file, as shown in the comment. Once you've deserialized it and converted it to a Pandas DataFrame, you can see detailed metrics for each of the iterations of the automated ML step.

The model file can be deserialized into a Model object that you can use for inferencing, further metrics analysis, and so forth.

import pickle

model_filename = model_output._path_on_datastore
# model_filename = path to downloaded file

with open(model_filename, "rb" ) as f:
    best_model = pickle.load(f)

# ... inferencing code not shown ...

For more information on loading and working with existing models, see Use an existing model with Azure Machine Learning.

Download the results of an automated ML run

If you've been following along with the article, you'll have an instantiated run object. But you can also retrieve completed Run objects from the Workspace by way of an Experiment object.

The workspace contains a complete record of all your experiments and runs. You can either use the portal to find and download the outputs of experiments or use code. To access the records from a historic run, use Azure Machine Learning to find the ID of the run in which you are interested. With that ID, you can choose the specific run by way of the Workspace and Experiment.

# Retrieved from Azure Machine Learning web UI
run_id = 'aaaaaaaa-bbbb-cccc-dddd-0123456789AB'
experiment = ws.experiments['titanic_automl']
run = next(run for run in ex.get_runs() if == run_id)

You would have to change the strings in the above code to the specifics of your historical run. The snippet above assumes that you've assigned ws to the relevant Workspace with the normal from_config(). The experiment of interest is directly retrieved and then the code finds the Run of interest by matching the value.

Once you have a Run object, you can download the metrics and model.

automl_run = next(r for r in run.get_children() if == 'AutoML_Classification')
outputs = automl_run.get_outputs()
metrics = outputs['default_metrics_AutoML_Classification']
model = outputs['default_model_AutoML_Classification']


Each Run object contains StepRun objects that contain information about the individual pipeline step run. The run is searched for the StepRun object for the AutoMLStep. The metrics and model are retrieved using their default names, which are available even if you don't pass PipelineData objects to the outputs parameter of the AutoMLStep.

Finally, the actual metrics and model are downloaded to your local machine, as was discussed in the "Examine pipeline results" section above.

Next Steps