MLflow provides a powerful platform that allows data scientists to log and track various metrics related to project modeling. It is highly useful for managing end-to-end machine learning workflows. MLflow simplifies experimentation, reproducibility, and deployment of machine learning models. When integrating with Azure, MLflow becomes an even more flexible tool. This is particularly handy when you’re trying to log metrics from a job run or analyzing the performance of the job.
In the context of Azure, specifically regarding exam DP-100 (Designing and Implementing a Data Science Solution on Azure), understanding how to incorporate MLflow can be a game-changer. This article will guide you on how you can utilize MLflow to log metrics from an Azure job run.
1. Installation of MLflow
Before using MLflow, you need to install it. You can either install MLflow by running “pip install mlflow” or using the Docker image provided by mlflow. Azure already includes MLflow in their pre-configured VMs and Databricks notebook environments.
2. Working with MLflow
In MLflow, you start by initiating an experiment, which groups together multiple runs based on the same parameters and settings for easy comparison. Metrics monitoring is one of the key functionalities of MLflow.
Below is a simple Python code snippet showing how you can use MLflow to log parameters, metrics and model details:
import mlflow
from sklearn.ensemble import RandomForestClassifier
with mlflow.start_run():
rf = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0)
rf.fit(X_train, y_train)
# Log parameters
mlflow.log_param("n_estimators", 100)
mlflow.log_param("max_depth", 2)
# Log metrics
train_accuracy = rf.score(X_train, y_train)
test_accuracy = rf.score(X_test, y_test)
mlflow.log_metric("Train Accuracy", train_accuracy)
mlflow.log_metric("Test Accuracy", test_accuracy)
# Log model
mlflow.sklearn.log_model(rf, "model")
In this example, we run a RandomForestClassifier model and log its parameters (n_estimators and max_depth), metrics (train and test accuracy), and model details using MLflow’s log_param, log_metric, and log_model methods respectively.
3. Running Jobs and Logging Metrics on Azure
When running a job on Azure, you need to configure and submit an Azure Machine Learning experiment. You can also create a Python script that defines the parameters, metrics, and models to be logged, and you can run this script as a job using the Azure Machine Learning SDK.
For instance, you can use the ScriptRunConfig class to define the script to run, and then use the submit method to run this script as a job:
from azureml.core import ScriptRunConfig, Experiment
# Create a ScriptRunConfig
src = ScriptRunConfig(source_directory='.',
script='log_metrics.py',
compute_target='aml-compute')
# Submit the experiment
run = Experiment(ws, 'MLflow_metrics_logging').submit(src)
# Wait for the run to complete
run.wait_for_completion(show_output=True)
In this example, we assume that the ‘log_metrics.py’ script includes the MLflow code to log metrics similar to the code snippet in the previous section.
Once the job is run on Azure, you can view the logged metrics in the MLflow UI on the Azure ML Studio.
In conclusion, MLflow provides a powerful platform for logging metrics and monitoring your machine learning experiments. Its seamless integration with Azure Machine Learning further enhances its capabilities. Hence, mastering the utilization of MLflow as a tool for logging metrics within the Azure environment can be a critical component when preparing for your DP-100: Designing and Implementing a Data Science Solution on Azure exam.
Practice Test
True or False: MLflow is a platform developed by Microsoft Azure for model management and deployment.
- True
- False
Answer: False
Explanation: MLflow is not developed by Microsoft Azure; it is an open-source platform designed by Databricks to manage the machine learning lifecycle, including experimentation, reproducibility, and deployment.
In Azure, is it possible to use MLflow to log metrics and parameters from your data science experiments?
- True
- False
Answer: True
Explanation: Azure Machine Learning service integrates with MLflow, enabling you to track metrics and parameters of your machine learning experiments.
MLflow’s tracking component can be used to log and query experiments. What data does it store during an experiment?
- A. Code Version
- B. Experiment Metadata
- C. Performance Metrics
- D. Model parameters
Answer: All of the above.
Explanation: The MLflow tracking component is an API to log and query machine learning experiments. It logs data that includes code version, experiment metadata, start and end time marked by time stamps, performance metrics, model parameters, and a lot more.
Which API does MLflow use to log parameters, code versions, metrics, and output files when running machine learning code and later visualize the results?
- A. Tracking API
- B. Data API
- C. Metrics API
- D. Logging API
Answer: A. Tracking API
Explanation: MLflow uses the Tracking API that allows you to log parameters, code versions, metrics, and output files when running your machine learning code and later visualize the results.
Which of the below options are suitable storage backends for MLflow?
- A. Local file path
- B. Azure Blob storage
- C. Amazon S3
- D. Google Drive
Answer: A. Local file path, B. Azure Blob storage, C. Amazon S3
Explanation: MLflow can use local file paths, Azure Blob storage, and Amazon S3 as storage backends. Google Drive is not supported.
In the context of MLflow, what is a “run”?
- A. A complete lifecycle of a Model
- B. A execution of a data version
- C. An execution of a particular set of parameters
- D. An execution of a data experiment
Answer: C. An execution of a particular set of parameters
Explanation: In MLflow terms, a “run” is an execution of a data science code with a specific set of parameters.
MLflow does not support tracking real-time metrics. True or False?
- True
- False
Answer: False
Explanation: MLflow does support tracking real-time metrics, which is essential for tracking the progress of long-running tasks.
MLflow does not support automatic logging for Azure Machine Learning service. True or False?
- True
- False
Answer: False
Explanation: MLflow supports automatic logging in Azure Machine Learning service which allows automated capture and logging of metrics, parameters, and artifacts.
In MLflow, which URI scheme should be used to store artifacts on Azure Blob Storage?
- A. wasbs://
- B. https://
- C. ftp://
- D. s3://
Answer: A. wasbs://
Explanation: The wasbs:// URI scheme is used to store artifacts on Azure Blob storage in MLflow.
MLflow’s model registry component allows you to:
- A. Save any dataframe as a model.
- B. Collaboratively manage the full lifecycle of an MLflow Model.
- C. Execute a range of Machine Learning experiments.
- D. Save the compute target for a model.
Answer: B. Collaboratively manage the full lifecycle of an MLflow Model.
Explanation: MLflow’s Model Registry component allows you to collaboratively manage the full lifecycle of a machine learning model, including model versioning, staging, and annotation.
Interview Questions
What is MLflow in the context of Azure?
MLflow is an open-source platform used to manage the machine learning lifecycle, including experimentation, reproducibility, and deployment.
What is the main function of MLflow Tracking in relation to logging metrics from a job run?
MLflow Tracking is used for recording and querying experiments. It includes logging parameters, versioning of datasets, tracking metrics, and output files to ensure that every ML model training run is documented in detail.
What are the three main components of MLflow?
The three main components of MLflow are MLflow Tracking, MLflow Projects, and MLflow Models.
How can you start an MLflow tracking server in Azure?
You can start an MLflow tracking server by running the mlflow server command and specifying the backend store and default artifact locations.
Which method is used to log metrics in MLflow?
The method to log metrics in MLflow is log_metric(). This method is wrapped around a script so that it logs customized metrics.
Is it possible to log multiple metrics over time in MLflow?
Yes, MLflow allows you to log multiple metrics over time for a single run. Metrics are key-value pairs that you can view in real time.
How can you organize runs in MLflow using tags?
With MLflow, you can use tags to annotate runs with specific metadata that is filterable and searchable.
How can you specify the run while logging metrics in MLflow?
While logging metrics, you can specify the run by using the start_run() function and passing the run ID.
Which component of MLflow is usually used with a tracking server to run and track machine learning code based on a project format?
MLflow Projects component is used with a tracking server to run and track machine learning code based on a project format.
What is the function of the log_params() method in MLflow Tracking?
The log_params() function in MLflow is used to log multiple parameters at once. It accepts a dictionary with key-value pairs for the parameters.
How can the results from MLflow be visualized?
Results from MLflow, including logged metrics, can be visualized in the MLflow tracking UI or using external visualization tools like TensorBoard.
What is the role of the log_artifacts() function in MLflow?
The log_artifacts() function in MLflow is used to log all the files in a given directory as artifacts of the run. The files are organized under the provided artifact_path in the backend store.
Can you use MLflow with Azure Machine Learning workspace?
Yes, Azure Machine Learning supports and integrates with MLflow to track experiments, store artifacts, and deploy models.
What is the purpose of the MLflow Models component in Azure?
MLflow Models component simplifies model deployment. It defines a standard model format allowing different deployment tools to understand the model, which helps in deploying them to various outputs.
Can MLflow be used with different machine learning libraries and algorithms in Azure?
Yes, MLflow is designed to work with any machine learning library and algorithm. It provides a way of storing parameters and metrics for different types of machine learning models in Azure.
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