This process allows you to check on the status and health of your data processing pipelines and take corrective action if needed. In the framework of the DP-100 Designing and Implementing a Data Science Solution on Azure exam, Azure provides a variety of tools and techniques to successfully monitor pipeline runs.
Understanding Azure data pipeline runs
Pipeline runs in Azure are instances of pipeline executions. Each run is related to a specific trigger, either manual or scheduled. Pipeline runs are defined by two key properties: the Run ID, a unique identifier for the pipeline run, and the status of the run (e.g., ‘Running’, ‘Succeeded’, or ‘Failed’).
Monitoring pipeline runs using Azure Portal
You can monitor pipeline runs directly from the Azure portal. Azure Data Factory provides a graphical interface to monitor and manage pipeline runs. By accessing the ‘Monitor’ section in the Azure Data Factory, you can see detailed and summarized views of all pipeline runs. You can customize these views to include metrics of interest, such as run start and end times, duration, and status.
More importantly, under the ‘Actions’ column, you can see specific actions related to each pipeline run, like viewing activity runs within the pipeline, rerunning the pipeline, or canceling ongoing pipeline runs.
Monitoring pipeline runs using Azure Monitor
Azure Monitor is another tool you can use to track your Azure data factory pipelines. Azure Monitor collects and aggregates logs and metrics from various Azure services. You can configure alerts based on these metrics or logs, making Azure Monitor a more proactive way to manage pipeline runs. For instance, you can set an alert for when a pipeline run fails, and this can trigger an email notification or even a Logic App for automated remediation steps.
Azure SDKs for monitoring pipeline runs
When it comes to programmatic monitoring of pipeline runs, Azure SDKs come in handy. Azure provides SDKs for various languages, including Python and .NET. These SDKs offer a plethora of methods and classes to extract pipeline run information and manipulate it as needed.
An example using Azure Python SDK:
from azureml.pipeline.core import PipelineRun
from azureml.core import Workspace
# Load Azure workspace
ws = Workspace.from_config()
# Retrieve a specific pipeline run
pipeline_run = PipelineRun(ws.experiments["Experiment_Name"], run_id="Run_ID")
# Get the details of the pipeline run
details = pipeline_run.get_details()
# Print the details
print(details)
In the aforementioned Python code, we first import necessary libraries and load our Azure workspace. We then retrieve a specific pipeline run using the run_id, and finally, we print the details of that pipeline run.
Conclusion
Monitoring pipeline runs is a vital aspect of implementing a reliable and robust data science solution on Azure. Azure offers flexibility in monitoring options, ranging from interactive visual interfaces to programmatically accessible SDKs. It could be beneficial to leverage a mix of these tools to maintain a healthy, seamless flow of data in your Data Factory pipelines. Knowing how to use these tools is paramount in successfully passing the DP-100 Azure exam and, more importantly, implementing efficient data solutions in real-life scenarios.
Practice Test
True or False: The Azure portal provides a graphical interface to monitor pipeline runs.
– True
– False
Answer: True
Explanation: Azure portal does provide a graphical interface where users can monitor pipeline runs and observe each pipeline’s step details.
Multiple select: Which of the following tasks can be accomplished from monitoring pipeline runs?
– a) View pipeline logs
– b) Cancel a running pipeline
– c) Re-run a pipeline
– d) Schedule future pipelines
Answer: a, b, c
Explanation: With the Azure portal, users can view pipeline logs, cancel a running pipeline, and re-run a pipeline. Scheduling future pipelines is not a function typically associated with monitoring current or past pipeline runs.
Single select: What tool can you use to access detailed log files related to pipeline runs?
– a. Azure DevOps
– b. Azure Monitor
– c. Azure Data Studio
– d. Azure Storage Explorer
Answer: b. Azure Monitor
Explanation: Azure Monitor collects detailed log data that can be analyzed for understanding specific problems and the overall performance of pipelines.
True or False: You should use Azure Data Factory to monitor pipeline runs for your Machine Learning models in Azure Machine Learning Studio.
– True
– False
Answer: False
Explanation: While Azure Data Factory is a service for creating data-driven workflows, monitoring pipelines runs is usually done via Azure Machine Learning Studio.
Single select: What is the function of ‘Metrics’ in Azure portal for monitoring pipeline runs?
– a. To identify the most expensive resources
– b. To identify slow performing features
– c. To identify the failed pipeline runs
– d. To identify storage locations
Answer: b. To identify slow performing features
Explanation: ‘Metrics’ in Azure portal can identify the slower performing operations or performance bottlenecks in a pipeline run.
Multiple select: Which of the following options does Azure Monitor provide for creating alerts?
– a. Log alerts
– b. Metric alerts
– c. Activity log alerts
– d. Database alerts
Answer: a, b, c
Explanation: Azure Monitor provides the options to create log alerts, metric alerts and activity log alerts. There is no such option for “database alerts”.
True or False: Running a published pipeline always requires manual triggering.
– True
– False
Answer: False
Explanation: Pipelines can be triggered manually, but also automatically through events or on a schedule.
Single select: The dashboard of Azure Machine Learning studio provides which of the following insights about pipeline runs?
– a. Costs of compute resources
– b. Numbers of experiments run
– c. Data quality metrics
– d. All of the above
Answer: b. Numbers of experiments run
Explanation: The dashboard in Azure Machine Learning studio provides information like the numbers of experiments that have been run and their respective statuses.
True or False: It is possible to set alerts on metrics for pipeline monitoring in Azure.
– True
– False
Answer: True
Explanation: Azure Monitor allows users to set alerts on specific metrics, notifying users when given conditions are met.
Single select: Which Azure feature allows you to visualize, query, and set alerts on your pipeline metrics?
– a. Azure DevOps
– b. Azure Monitor Logs
– c. Azure Data Factory
– d. Azure Machine Learning Studio
Answer: b. Azure Monitor Logs
Explanation: Azure Monitor Logs is a feature that allows users to visualize, query, and set alerts on their metrics data.
True or False: Azure Pipelines is a cloud service that you can use to automatically build, test, and deploy your code to any platform.
– True
– False
Answer: True
Explanation: Azure Pipelines is a cloud service that can be used to automatically compile (build), validate (test), and deploy applications.
Interview Questions
What is a pipeline run in Azure ML?
A pipeline run in Azure ML refers to the execution of a pipeline, which involves the running of a series of steps defined in the pipeline. Each run is linked with specific data, experiments, and models that can be tracked in the Azure ML studio.
How can you monitor the status of a pipeline run in Azure ML?
The RunDetails widget in Azure ML SDK is used for monitoring the status of a pipeline run. The widget visualizes the step runs and sub-runs as a graph and refreshes the state every 15 seconds to provide real-time updates.
What role does Azure Monitor play in pipeline runs?
Azure Monitor collects, analyzes, and acts on telemetry data from your Azure and non-Azure environments, helping you understand how your applications are performing and proactively identifying issues affecting them and the resources they depend on, including pipeline runs.
How can you access the logs for a pipeline run in Azure ML?
Logs for a pipeline run can be accessed through Azure ML Studio, under the ‘Experiments’ tab, where you can select the relevant pipeline run. They can also be accessed programmatically with the .get_logs() function in the Azure ML SDK.
What is the purpose of the RunDetails widget in Azure ML?
The RunDetails widget is used to monitor the progress of a pipeline run. It visualizes the steps and sub-steps of a pipeline run in a graph layout and refreshes the states every 15 seconds, providing real-time updates of the progress.
How can we monitor pipeline performance in Azure ML?
Pipeline performance can be monitored using built-in monitoring tools in Azure ML Studio, such as the RunDetails widget and the Metrics tab. Additionally, Azure Monitor and Azure Log Analytics can provide insights into the pipeline’s performance.
How to enable Application Insights for Azure ML?
Application Insights can be enabled by setting the `collect_model_data` parameter to `True` when creating or updating a Workspace. Once enabled, it collects different telemetry data including pipeline runs, which helps in diagnosing problems and understanding user behaviors.
What is Azure Log Analytics?
Azure Log Analytics is a service in Azure Monitor that helps you collect and analyze data generated by resources in your cloud and on-premises environments. It gives you real-time insights using integrated search and custom dashboards to readily analyze millions of records.
What does the `get_metrics()` function in Azure ML do?
The `get_metrics()` function in Azure ML retrieves the metrics recorded during a pipeline run. These metrics can include accuracy, precision, recall, or any other metrics that were recorded using the `log()` function during the pipeline run.
How can you track custom metrics of a pipeline run in Azure ML?
Custom metrics can be tracked by using the `log()` function in an estimator’s script in Azure ML. These metrics can subsequently be viewed in the studio or retrieved programmatically using the `get_metrics()` function.
Can you generate a Pipeline run using a REST API in Azure ML?
Yes, Azure ML allows you to generate a Pipeline run using a REST endpoint. For this, you need to publish a Pipeline and Azure ML will provide a REST endpoint that can trigger the pipeline run.
How do you publish a pipeline in Azure ML?
A pipeline can be published in Azure ML by using the `publish()` method of the Pipeline object. This creates a REST endpoint that can be used to trigger the pipeline for execution.
What are steps in Azure ML Pipeline?
Steps are the individual tasks that a pipeline performs in Azure ML. They can be data preparation tasks, training tasks, or scoring tasks. Each step can run independently and can have its own compute context.
Can you cancel a Pipeline run in Azure ML?
Yes, you can cancel a Pipeline run in Azure ML using the `cancel()` method. It stops the execution of the current pipeline and all its associated steps.
How can we diagnose errors in a Pipeline run in Azure ML?
Errors in a Pipeline run can be diagnosed by reviewing the logs, which are accessible via Azure ML Studio or SDK’s `get_logs()` function. Additionally, Azure Application Insights can be used to monitor and diagnose errors in real-time.
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