Use MLflow to log metrics from a job run

MLflow is an open-source platform that allows data scientists to track, manage, and deploy machine learning models. It provides a consistent way to log parameters, metrics, and artifacts from various stages of the machine learning lifecycle. In this article, we will explore how MLflow can be used to log metrics from a job run related to the exam topic “Designing and Implementing a Data Science Solution on Azure.”

Installing MLflow

To get started, make sure you have MLflow installed. You can install MLflow using pip by running the following command:

!pip install mlflow

Logging Metrics with MLflow

Once MLflow is installed, we can start logging metrics from our job run. First, we need to initialize the MLflow run. The run serves as a container for all the information we want to log. We can start a new run using the mlflow.start_run() function:

import mlflow
# Start a new MLflow run
mlflow.start_run()

Next, we can log metrics during the execution of our job. For example, let’s say we want to log the accuracy and loss values of our model:

accuracy = 0.85
loss = 0.45

We can log metrics using the mlflow.log_metric() function:

# Log metrics
mlflow.log_metric("accuracy", accuracy)
mlflow.log_metric("loss", loss)

Once our job is complete, we can stop the MLflow run using the mlflow.end_run() function:

# End the MLflow run
mlflow.end_run()

Using MLflow with Azure Machine Learning (AML)

By default, MLflow logs the metrics, parameters, and artifacts to the local file system. However, MLflow also supports other tracking backends such as Azure Blob Storage and Databricks. You can refer to the MLflow documentation to learn how to configure and use different tracking backends.

To log metrics from Azure Machine Learning (AML) experiments, you can leverage the MLflow integration with AML. MLflow provides a Python package called mlflow.azureml that lets you work seamlessly with MLflow and AML experiments.

To log metrics from an AML experiment, you can follow these steps:

1. Create an AML workspace: If you don’t have an AML workspace, you can create one using the Azure portal or Azure CLI.
2. Configure your workspace: Once the workspace is created, you need to configure your workspace by authenticating yourself. You can use the azureml-core Python package for authentication.
3. Start an experiment: In your job script, import mlflow.azureml and start an AML experiment using mlflow.azureml.start_run():

from azureml.core import Experiment
import mlflow.azureml

experiment_name = 'my-experiment'
experiment = Experiment(workspace=aml_workspace, name=experiment_name)

# Start an AML experiment
mlflow.azureml.start_run(experiment)

4. Log metrics: Inside your job script, you can log metrics and parameters as discussed earlier using mlflow.log_metric() and mlflow.log_param().
5. End the MLflow run: To end the MLflow run and log the metrics to your AML experiment, use mlflow.azureml.end_run():

# End the MLflow run and log metrics to AML experiment
mlflow.azureml.end_run()

That’s it! You have now successfully logged metrics from your job run related to the exam topic “Designing and Implementing a Data Science Solution on Azure” using MLflow. MLflow provides a simple and standardized way to track and organize your machine learning experiments, making it easier for you to reproduce and share your results.

Remember to explore the MLflow documentation for more advanced features such as logging artifacts, creating and managing MLflow experiments, and integrating MLflow with other Azure services like Azure Databricks and Azure Machine Learning.

Happy tracking!