Create and run a machine learning pipeline using Azure Machine Learning SDK

In this article, you learn how to create, publish, run, and track a machine learning pipeline using the Azure Machine Learning SDK. These pipelines help create and manage the workflows that stitch together various machine learning phases. Each phase of a pipeline, such as data preparation and model training, can include one or more steps.

The pipelines you create are visible to the members of your Azure Machine Learning service workspace.

Pipelines use remote compute targets for computation and the storage of the intermediate and final data associated with that pipeline. Pipelines can read and write data to and from supported Azure storage locations.



If you don’t have an Azure subscription, create a free account before you begin. Try the free or paid version of Azure Machine Learning service today.

Set up machine learning resources

Create the resources required to run a pipeline:

  • Set up a datastore used to access the data needed in the pipeline steps.

  • Configure a DataReference object to point to data that lives in or is accessible in a datastore.

  • Set up the compute targets on which your pipeline steps will run.

Set up a datastore

A datastore stores the data for the pipeline to access. Each workspace has a default datastore. You can register additional datastores.

When you create your workspace, an Azure file storage and a blob storage are attached to the workspace by default. Azure file storage is the "default datastore" for a workspace, but you can also use blob storage as a datastore. Learn more about Azure storage options.

# Default datastore (Azure file storage)
def_data_store = ws.get_default_datastore() 

# The above call is equivalent to this 
def_data_store = Datastore(ws, "workspacefilestore")

# Get blob storage associated with the workspace
def_blob_store = Datastore(ws, "workspaceblobstore")

Upload data files or directories to the datastore for them to be accessible from your pipelines. This example uses the blob storage version of the datastore:


A pipeline consists of one or more steps. A step is a unit run on a compute target. Steps might consume data sources and produce “intermediate” data. A step can create data such as a model, a directory with model and dependent files, or temporary data. This data is then available for other steps later in the pipeline.

Configure data reference

You just created a data source that can be referenced in a pipeline as an input to a step. A data source in a pipeline is represented by a DataReference object. The DataReference object points to data that lives in or is accessible from a datastore.

blob_input_data = DataReference(

Intermediate data (or output of a step) is represented by a PipelineData object. output_data1 is produced as the output of a step and used as the input of one or more future steps. PipelineData introduces a data dependency between steps and creates an implicit execution order in the pipeline.

output_data1 = PipelineData(

Set up compute

In Azure Machine Learning, compute (or compute target) refers to the machines or clusters that will perform the computational steps in your machine learning pipeline. For example, you can create an Azure Machine Learning Compute for running your steps.

compute_name = "aml-compute"
 if compute_name in ws.compute_targets:
    compute_target = ws.compute_targets[compute_name]
    if compute_target and type(compute_target) is AmlCompute:
        print('Found compute target: ' + compute_name)
    print('Creating a new compute target...')
    provisioning_config = AmlCompute.provisioning_configuration(vm_size = vm_size, # NC6 is GPU-enabled
                                                                min_nodes = 1, 
                                                                max_nodes = 4)
     # create the compute target
    compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)

    # Can poll for a minimum number of nodes and for a specific timeout. 
    # If no min node count is provided it will use the scale settings for the cluster
    compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)

     # For a more detailed view of current cluster status, use the 'status' property    

Construct your pipeline steps

Now you are ready to define a pipeline step. There are many built-in steps available via the Azure Machine Learning SDK. The most basic of these steps is a PythonScriptStep that executes a Python script in a specified compute target.

trainStep = PythonScriptStep(
    arguments=["--input", blob_input_data, "--output", processed_data1],

After you define your steps, you build the pipeline using some or all of those steps.


No file or data is uploaded to Azure Machine Learning service when you define the steps or build the pipeline.

# list of steps to run
compareModels = [trainStep, extractStep, compareStep]

# Build the pipeline
pipeline1 = Pipeline(workspace=ws, steps=[compareModels])

Submit the pipeline

When you submit the pipeline, the dependencies are checked for each step and a snapshot of the folder specified as the source directory is uploaded to Azure Machine Learning service. If no source directory is specified, the current local directory is uploaded.

# Submit the pipeline to be run
pipeline_run1 = Experiment(ws, 'Compare_Models_Exp').submit(pipeline1)

When you first run a pipeline:

  • The project snapshot is downloaded to the compute target from blob storage associated with the workspace.
  • A docker image is built corresponding to each step in the pipeline.
  • The docker image for each step is downloaded to the compute target from the container registry.
  • If a DataReference object is specified in a step, the data store is mounted. If mount is not supported, the data is instead copied to the compute target.
  • The step runs in the compute target specified in the step definition.
  • Artifacts such as logs, stdout and stderr, metrics, and output specified by the step are created. These artifacts are then uploaded and kept in the user’s default data store.

run an experiment as a pipeline

Publish a pipeline

You can publish a pipeline to run it with different inputs later. For the REST endpoint of an already published pipeline to accept parameters, the pipeline must be parameterized before publishing.

  1. To create a pipeline parameter, use a PipelineParameter object with a default value.

    pipeline_param = PipelineParameter(
  2. Add this PipelineParameter object as a parameter to any of the steps in the pipeline as follows:

    compareStep = PythonScriptStep(
      arguments=["--comp_data1", comp_data1, "--comp_data2", comp_data2, "--output_data", out_data3, "--param1", pipeline_param],
      inputs=[ comp_data1, comp_data2],
  3. Publish this pipeline that will accept a parameter when invoked.

published_pipeline1 = pipeline1.publish(
    description="My Published Pipeline Description")

Run a published pipeline

All published pipelines have a REST endpoint to invoke the run of the pipeline from external systems such as non-Python clients. This endpoint provides a way for "managed repeatability" in batch scoring and retraining scenarios.

To invoke the run of the preceding pipeline, you need an Azure Active Directory authentication header token as described in AzureCliAuthentication class

response =, 
    json={"ExperimentName": "My_Pipeline",
        "ParameterAssignments": {"pipeline_arg": 20}})

View results

See the list of all your pipelines and their run details:

  1. Sign in to the Azure portal.

  2. View your workspace to find the list of pipelines. list of machine learning pipelines

  3. Select a specific pipeline to see the run results.

Next steps

Learn how to run notebooks by following the article, Use Jupyter notebooks to explore this service.