Azure Machine Learning Pipelines

Independent

Reusable

Assorted Computation

Modular

Versioning and Tracking

Partnership

Creating Pipelines

ws = Workspace.from_config()
blob_store = Datastore(ws, "workspaceblobstore")
compute_target = ws.compute_targets["STANDARD_NC6"]
experiment = Experiment(ws, 'MyExperiment')
input_data = Dataset.File.from_files(DataPath(datastore, '20newsgroups/20news.pkl'))
prepped_data_path = OutputFileDatasetConfig(name = "output_path")
dataprep_step = PythonScriptStep(name = "prep_data", script_name = "dataprep.py", source_directory = "prep_src", compute_target = compute_target, arguments = ["--prepped_data_path", prepped_data_path], inputs = [input_dataset.as_named_input('raw_data').as_mount()])
prepped_data = prepped_data_path.read_delimited_files()
train_step = PythonScriptStep(name = "train", script_name = "train.py", compute_target = compute_target, arguments = ["--prepped_data", prepped_data], source_directory = "train_src")
steps = [dataprep_step, train_step]
pipeline = Pipeline(workspace = ws, steps = steps)
pipeline_run = experiment.submit(pipeline)
pipeline_run.wait_for_completion()

Tasks Machine Learning Pipeline can Focus Upon

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Man on a Mission - to create epochal impact

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