Train a model by using Python SDKv2

Azure Machine Learning Python SDKv2 acts as a tool to help build, train, and deploy your machine learning models. This upgraded version of the SDK comes with a simplified experience of handling machine learning workloads, better integration with native Azure services, and other improvements that deliver a better user experience.

To install SDKv2, get the latest azure-ml package using pip.

pip install azure-ml

Once you have installed the SDK, you can easily follow the steps outlined below to train a model using this SDK.

Training a Model using Python SDKv2

The process of training a model involves data loading, model creation, and model training. Let’s follow these steps meticulously:

1. Data loading

   The first step in training a model involves loading your dataset. You can load data directly from Azure datasets or local CSV files.

   Here is how you can do this using Python SDK v2:

   import pandas as pd
from azure.ml.core import Dataset

   # from Azure datasets
   data = Dataset.get_by_name(workspace, "your-dataset-name")

   # from local CSV files
data = pd.read_csv('data.csv')

2. Model creation

   The second step involves creating your model. This step entirely hinges on the type of problem you want to solve. Below is an example of how you create a model in Python SDKv2:

   from sklearn.ensemble import RandomForestRegressor

   # Initialising the model
model = RandomForestRegressor(n_estimators=100, random_state=42)

3. Model training

   This is the most crucial part of the entire process—where we train the model using our data. Here’s a code snippet showing how to do this:

   from sklearn.model_selection import train_test_split

   # train test split
   X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=123)

   # train the model
model.fit(X_train, y_train)

Remember, while code examples are given here for illustrative understanding, the actual implementation will depend significantly on the exact nature of the problem and the data at hand.

Comparing Model Performance

It’s essential to evaluate the performance of your model. The specific metric you use to evaluate your model will depend on your problem statement. Below is how you can make predictions and check the accuracy score:

from sklearn.metrics import mean_squared_error

# evaluate the model
mse = mean_squared_error(y_test, y_pred)
print(mse)

This provides an introduction to the journey of Designing and Implementing a Data Science Solution on Azure. While getting familiar with these steps and understanding Python SDKv2, it is equally essential to gain hands-on experience. Implement examples, play around with different datasets and machine learning models, and make a thorough understanding of the underlying process. Your mastery in these areas would see you excel in the DP-100 Designing and Implementing a Data Science Solution on Azure exam.

Practice Test

True or False: Python SDKv2 allows you to train multiple models simultaneously.

• True
• False

Answer: False

Explanation: Python SDKv2 in Azure ML allows training of a single model at a time. However, you can use HyperDrive to tune hyperparameters across multiple model runs.

What is Python SDKv2 in Azure Machine Learning?

• A. Software Development Kit for building Machine Learning models
• B. An extension for the Azure portal
• C. An IDE for Azure Machine Learning
• D. Python language support in Azure

Answer: A. Software Development Kit for building Machine Learning models

Explanation: Python SDKv2 is a package provided by Azure Machine Learning to develop, train, test and deploy ML models using Python.

True or False: Python SDKv2 is exclusively for creating machine learning models.

• True
• False

Answer: False

Explanation: While Python SDKv2 is commonly used for creating machine learning models, it also allows you to work with datasets, compute resources, and pipelines amongst others.

Which of the following are you able to do with Python SDKv2? (Select all that apply)

• A. Train models
• B. Query datasets
• C. Deploy models
• D. Compile python scripts

Answer: A. Train models, B. Query datasets, C. Deploy models

Explanation: Python SDKv2 allows you to train and deploy models, and work with datasets in Azure ML. You cannot compile python scripts using it.

True or False: You can use Python SDK to access and manage Azure resources directly.

• True
• False

Answer: True

Explanation: Python SDK allows you to script actions that can be done in Azure portal. This includes accessing and managing Azure resources.

You can use the Azure CLI in combination with Python SDKv True or False?

• True
• False

Answer: True

Explanation: The Azure CLI can be used alongside Python SDKv2 to script and automate tasks within Azure ML.

What are run configurations in Azure ML Python SDKv2?

• A. They define the Python environment for the run
• B. They define the hardware specifications for the run
• C. They define the script parameters for the run
• D. All of the above

Answer: D. All of the above

Explanation: Run configurations in Python SDKv2 define the Python environment, including packages and versions, the computational resources (hardware) and the script parameters for the run.

To be able to use Python SDKv2, Azure subscription is mandatory. True or False?

• True
• False

Answer: True

Explanation: To utilize Python SDKv2 for Azure ML, an active Azure subscription is required because the experiments you run and the resources are associated with your Azure subscription.

The Python SDKv2 corresponds to which tab in Azure ML studio?

• A. Designer
• B. Notebooks
• C. Automated ML
• D. Pipelines

Answer: B. Notebooks

Explanation: Python SDKv2 scripts are typically written and executed in the Notebooks tab of the Azure ML studio.

True or False: You can run Python SDKv2 scripts on both your local machine and Azure compute instances.

• True
• False

Answer: True

Explanation: You can run Python SDKv2 scripts on Azure ML compute instances, providing the required RAM and CPU for the experiment, or on your local machine, although this might require additional installs.

Interview Questions

What is the Python SDKv2 for Azure Machine Learning?

The Python SDKv2 for Azure Machine Learning is a package that allows developers to build, train, manage, and deploy machine learning models using the Azure platform. It provides necessary tools and functionalities to handle all the aspects of machine learning workflows.

What are the prerequisites to use the Python SDKv2 for Azure Machine Learning?

The prerequisites to use Python SDKv2 for Azure Machine Learning include having a valid Azure subscription, an Azure Machine Learning workspace, Python installed on your local machine, and installation of the Azure ML SDK for Python.

How can you install the Python SDKv2 for Azure Machine Learning?

You can install Python SDKv2 for Azure Machine Learning using pip, the Python package installer, with a command:

pip install azureml-sdk

.

How do you authenticate to a workspace using Python SDKv2?

You can use the

Workspace

class in the SDK to authenticate to a workspace. You will need your subscription id, resource group, and workspace name as parameters.

Can you use the PythonSDKv2 for real-time scoring?

Yes, Azure's Python SDKv2 allows you to deploy your model as a web service for real-time scoring.

How can you submit a training job to an Azure Machine Learning workspace?

You can submit a training job to an Azure Machine Learning workspace using the

experiment.submit()

method in the Python SDKv2.

What is the significance of the 'Experiment' class in Azure SDKv2?

The 'Experiment' class in Azure SDKv2 is used to manage, run, and analyze experiments. It also helps to track the runs of experiments.

How can you monitor your training job in Azure SDKv2?

You can monitor your training job with built-in Azure ML Studio or programmatically by using

RunDetails(run).show()

in the Python SDKv2.

What is the role of

RunConfiguration

in Python SDKv2?

RunConfiguration

in Python SDKv2 is used to define the run-time environment to execute scripts. It contains the set of instructions which specify the environment for the run.

How can you manage data in Azure Machine Learning using Python SDKv2?

Python SDKv2 provides the

Dataset

class to represent data in Azure Machine Learning. You can create, register, and retrieve datasets in Azure ML workspace with it.

How can you register a model with Python SDKv2?

You can register a model using the

Model.register()

static method in Python SDKv2.

Can you use Python SDKv2 for batch scoring?

Yes, you can use Python SDKv2 to deploy your trained model for batch scoring.

Why should you use Pipeline class in Python SDKv2?

Pipeline class in Python SDKv2 helps to define a sequence of steps to be executed in a specific order. It supports reusability and simplifies the process, ensuring consistency and reliability in machine learning tasks.

What is a Compute target in Python SDKv2, and when is it used?

Compute target in Python SDKv2 refers to the server or cluster where you run your training script or host your service deployment. It is used when you need to train a model or deploy a model on the Azure platform.

How can you handle deployment process using Python SDKv2?

Python SDKv2 offers

Model.deploy()

method, which allows you to deploy an instance of a model as a web service in Azure. You can monitor the status of the deployment using

service.get_logs()

method.