This process essentially means setting up an automated system that can transform raw input data into a set of desired output batch predictions based on the applied model. The DP-100 Designing and Implementing a Data Science Solution on Azure exam majorly focuses on, among other things, this aspect of deploying models to batch endpoints.
Let’s dive in and understand how to deploy a machine learning model to a batch endpoint in Azure Machine Learning.
What is a Batch Endpoint?
Before we delve into the deployment process, it is crucial to understand what a batch endpoint is. An endpoint in Azure Machine Learning is an interface that connects your model with the scoring code (also known as model prediction logic).
A batch endpoint, particularly, enables you to run batch predictions on a large amount of data rather easily. It is advantageous when you have a sizeable amount of data that doesn’t require real-time processing.
How to Deploy a Model to a Batch Endpoint
Now that we understand what a batch endpoint is let’s dive into the steps involved in deploying the model.
Step 1: Creating a Scoring Script
A scoring script is required when deploying the model. This script contains two necessary functions: init() and run(). The init() function is used to initialize your model, and the run() function is for your prediction logic.
Here’s an example of a scoring script:
def init():
# Load the model from the file
global model
model = joblib.load(Model.get_model_path('your_model_name'))
def run(input_data):
# Predict and return the results
data = json.loads(input_data)
result = model.predict(data)
return json.dumps(result.tolist())
Step 2: Create an Inference Configuration
The inference configuration describes how to configure the model to make predictions. This configuration includes the scoring script, environment (that comprises Python packages for model execution), and concurrency configuration.
Here’s how to build an inference configuration:
from azureml.core.model import InferenceConfig
from azureml.core.environment import Environment
from azureml.core.conda_dependencies import CondaDependencies
# Define an environment
env = Environment('deploytoenv')
env.python.conda_dependencies = CondaDependencies.create(pip_packages=['azureml-defaults', 'inference-schema'])
# Define the scoring script and environment in inference configuration
inference_config = InferenceConfig(entry_script='scoring_script.py', environment=env)
Step 3: Deploy the Model
The final step involves deploying the model as a web service to an Azure Container Instance (ACI) or Azure Kubernetes Service (AKS). Azure ML provides a convenient method “deploy” that we will use here.
from azureml.core.webservice import AciWebservice
from azureml.core.model import Model
deployment_config = AciWebservice.deploy_configuration(cpu_cores = 1, memory_gb = 1)
model = Model(ws, 'your_model_name')
service = Model.deploy(ws, 'mymodelservice', [model], inference_config, deployment_config)
service.wait_for_deployment(show_output = True)
The “wait_for_deployment” function will display the logs of your deployment process and might take a few minutes. Once the deployment is successful, you will see a message stating that.
Deploying a model to batch endpoints is a crucial component of the DP-100 exam. Understanding the process and the associated code will be pivotal to acing the exam. It’s always advisable to work and familiarize yourself with these on a practical level to gain deeper knowledge.
Remember, practice and hands-on experience are key to mastering Azure Machine Learning. Happy Learning and All the best for your DP-100 exam!
Practice Test
True or False: In Azure, deploying a model to a batch endpoint involves creating a real-time inference endpoint.
- False
Answer: False
Explanation: A batch endpoint, not a real-time endpoint, is used for batch scoring.
You can only deploy a model to a batch endpoint in Azure if your data is in real-time.
- False
Answer: False
Explanation: A batch endpoint is used for batch data, not real-time data.
True or False: In Azure, a batch endpoint can allow for parallel processing of multiple data requests at the same time.
- True
Answer: True
Explanation: Batch endpoint allows for parallel processing which is useful for large batches of data.
Which of the following is NOT a step in deploying a model to a batch endpoint in Azure?
- a) Register the model
- b) Create and configure the scoring script
- c) Train the model
- d) Define the inference configuration
Answer: c) Train the model
Explanation: Training the model is not a step in deploying a model to a batch endpoint. This is done prior to deployment.
True or False: To provide inputs to the scoring script at runtime, you should deploy the model to a batch endpoint.
- True
Answer: True
Explanation: Deploying a model to a batch endpoint allows for providing inputs to the scoring script at runtime.
Multiple models can be deployed to a single batch endpoint in Azure.
- True
Answer: True
Explanation: In Azure, a batch endpoint accommodates multiple models.
You can use Azure Machine Learning SDK to deploy a model to a batch endpoint.
- a) True
- b) False
Answer: a) True
Explanation: Azure Machine Learning SDK lets you deploy models to a batch endpoint.
For deploying a model to a batch endpoint in Azure, which scripting language can be used to write the scoring script?
- a) Python
- b) Ruby
- c) JavaScript
- d) C++
Answer: a) Python
Explanation: The scoring script for an Azure batch endpoint is typically written in Python.
True or False: To deploy a model to a batch endpoint, the model must first be registered in Azure Machine Learning workspace.
- True
Answer: True
Explanation: A registered model is required for deployment to a batch endpoint.
Which of the following are options for scaling a batch endpoint in Azure?
- a) Increase the number of compute nodes
- b) Increase the batch size
- c) Increase the memory
- d) All of the above
Answer: d) All of the above
Explanation: All these methods can be used for scaling a batch endpoint in Azure.
The batch endpoint in Azure uses HTTP to receive scoring requests.
- a) True
- b) False
Answer: a) True
Explanation: The batch endpoint is a RESTful service that receives HTTP requests.
The deployed models on a batch endpoint in Azure remain online and ready to process data continuously.
- a) True
- b) False
Answer: b) False
Explanation: Batch endpoint models are not kept online continuously, they are activated whenever a batch scoring request is made.
True or False: Once a model is deployed on a batch endpoint in Azure, it cannot be updated or modified.
- False
Answer: False
Explanation: Models deployed on batch endpoints can be updated or modified as needed.
The main advantage of deploying a model to a batch endpoint instead of a real-time endpoint is:
- a) Cost Efficiency
- b) Speed
- c) Real-time predictions
- d) All of the above
Answer: a) Cost Efficiency
Explanation: Deploying to a batch endpoint is more cost-efficient as it it used for batch processing of large volumes of data, not real-time predictions.
True or False: For a model to be deployed to a batch endpoint in Azure, it must first be trained using Azure Machine Learning Studio.
- False
Answer: False
Explanation: The model can be trained elsewhere, it just has to be registered in the Azure Machine Learning workspace before deployment to a batch endpoint.
Interview Questions
What is the purpose of deploying a model to a batch endpoint in Azure?
Deploying a model to a batch endpoint in Azure allows you to process large volumes of data asynchronously, which is useful for tasks that do not require real-time results, such as daily or weekly reporting.
What are the steps to deploy a model to a batch endpoint in Azure Machine Learning?
The steps to deploy a model to a batch endpoint in Azure Machine Learning are: train your model, register the model, define the inference configuration, configure the batch endpoint, and finally deploy the model to the endpoint.
What is the function of the inference configuration when deploying a model to a batch endpoint?
The inference configuration describes how to set up the web-service containing your model. It encapsulates the entry script and runtime environment that is used to run the model.
How do you ensure a secure connection while deploying a model to a batch endpoint?
Azure Machine Learning helps ensure a secure connection by providing authenticity and privacy using SSL/TLS when deploying a model to a batch endpoint.
What is the purpose of the parameter –query primaryKey in the az ml endpoint create command?
The –query primaryKey parameter is used to retrieve the primary key for the deployed endpoint, which is required to make predictions via the REST endpoint.
How can you monitor the performance of a deployed model in Azure?
You can monitor the performance of a deployed model in Azure by using Azure Machine Learning’s built-in model monitoring capabilities or by integrating it with Azure Monitor and Azure Log Analytics.
Can you deploy a model directly from your training run to a batch endpoint?
No, before deploying a model to a batch endpoint, you need to register the trained model in Azure Machine Learning Model registry.
What is ParallelRunStep for in batch endpoints?
ParallelRunStep in Azure Machine Learning is used for scoring or processing large amounts of data in parallel by distributing data across various nodes.
What format is used for data input in a batch endpoint?
For batch endpoints, data input should be a reference to Azure Blob storage in the form of an Azure Storage data reference.
Why might you use a Java prediction script when deploying a model?
Using a Java prediction script can be beneficial if you have specific library dependencies that are better managed in Java or if your model was developed in a language that Azure Machine Learning natively doesn’t support.
What is the workflow after a model is deployed to a batch endpoint?
After a model is deployed to a batch endpoint, you can send data via an HTTP POST request. The job will run asynchronously and once complete, the results will be stored in Azure Blob Storage.
What Azure resource is used to compute the operations once a batch endpoint is deployed?
Azure Machine Learning Compute Cluster is used to process the operations once a batch endpoint is deployed.
Can Azure Databricks be used as compute target during deployment of the model in Azure Machine Learning?
Yes, Azure Databricks can be used as a compute target to run the batch inferencing pipeline for model deployment in Azure Machine Learning.
How are errors handled during batch prediction?
In case of errors during batch prediction, Azure Batch AI provides stack traces or container logs for debugging purpose. Additionally, you can create alerts and metrics for monitoring purposes.
Can you redeploy the same model version to the batch endpoint, after making changes to the entry script or inference config?
Yes, you can redeploy the same model version to the batch endpoint after making changes to the entry script or inference config. Azure allows redeployment to reflect updated configurations.
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