The idea of model deployment revolves around making your trained ML model available in the production environment where it can take in inputs (features) and return outputs (predictions). The DP-100: Designing and Implementing a Data Science Solution on Azure exam, puts special emphasis on understanding model deployment requirements.
The process of model deployment in Azure generally involves the following steps:
- Registering the Model.
- Creating a Scoring Script.
- Creating an Environment.
- Creating and Deploying the Inference Configuration.
- Testing the Deployed Model.
1. Registering the Model
Before deploying a model, you need to register it in Model Management in your Azure Machine Learning workspace. The registered model is essentially a version of a trained model. Registering a model allows you to track its usage across multiple workspaces and experiments.
The model could have been trained inside or outside of Azure ML. If it was trained outside, you would need to ensure that it’s compatible with the framework that you’re using (Scikit-learn, TensorFlow, PyTorch, etc.).
Example code for model registration:
from azureml.core.model import Model
model = Model.register(model_path = “path to your local model”,
model_name = “name of the model”,
tags = {‘area’: “area of application”, ‘type’: “classification”},
description = “a brief description”,
workspace = ws)
2. Creating a Scoring Script
The scoring script is what the web service will use to make predictions. It typically contains two methods:
- init(): This method initializes the model, and will be called once when the service is started.
- run(raw_data): This method uses the model to predict the output based on the input data.
Example code for the scoring script:
def init():
global model
model_path = Model.get_model_path(‘your model name’)
model = joblib.load(model_path)
def run(raw_data):
data = np.array(json.loads(raw_data)[‘data’])
predictions = model.predict(data)
return predictions.tolist()
3. Creating an Environment
The scoring script operates inside an Azure ML environment. This environment encapsulates all dependencies necessary for replicable model scoring.
from azureml.core import Environment
env = Environment.from_conda_specification(name=’my_env’,
file_path=’path to conda specification file’)
4. Creating and Deploying the Inference Configuration
Inference configuration describes how to set up the web-service containing your model. It’s used later, when you deploy the model.
The inference configuration specifies the model, the scoring script, and the environment in which they should run.
from azureml.core.model import InferenceConfig
inference_config = InferenceConfig(entry_script=’scoring script path’,
environment=env)
5. Testing the Deployed Model
After deployment, you should test the model to make sure it’s performing as expected.
A model deployment on Azure is a RESTful service endpoint, and you may utilize the Azure SDK to consume it, or just make HTTP requests from any programming language of your choice.
import json
test_sample = json.dumps({‘data’: [
[0.0380759064334241, 0.0506801187398187, 0.0616962065186885, 0.0218723549949558, -0.0442234984244464,
-0.0348207628376986, -0.0434008456520269, -0.00259226199818282, 0.0199072074677015, -0.0176461251598052]
]})
prediction = service.run(test_sample)
Testing ensures that predictions made by the model are correct and the model is ready for use.
In conclusion, model deployment on Azure involves the registration of the model, creating a scoring script, setting up the environment, creating an inference configuration, and testing the service. Each of these steps has its own requirements and all of them together ensure a successful model deployment on Azure.
Practice Test
True/False: Model deployment in Azure requires a trained and validated machine learning model.
- True
- False
Answer: True.
Explanation: Before deployment, a model must be trained and validated to ensure it’s ready to make accurate predictions.
Which of the following can be used for real-time model deployment in Azure?
- a. Azure Functions
- b. Azure Machine Learning Web Service
- c. Azure Batch AI
- d. Azure Databricks
Answer: a, b.
Explanation: Azure Functions and Azure Machine Learning Web Service support real-time model deployment by serving predictions in real time.
True/False: It is not necessary to measure the performance of a deployed model in a production environment.
- True
- False
Answer: False.
Explanation: It is crucial to constantly monitor and measure a model’s performance after deployment to ensure it continues delivering accurate predictions.
Multi-selection: Which of the following release strategies can be used when deploying a model in Azure?
- a. Blue/Green Deployment
- b. Canary Release
- c. Parallel Run
- d. Red/Black Deployment
Answer: a, b, c.
Explanation: Blue/Green deployment, Canary release, and Parallel run are all recognized strategies for minimizing risk during deployment in Azure.
True/False: The deployment configuration for an Azure Machine Learning model includes information such as the compute target and the number of cores/CPU utilized.
- True
- False
Answer: True.
Explanation: The deployment configuration determines the compute resources allocated for the deployed model.
Which service in Azure is specifically designed for model deployment and management?
- a. Azure Machine Learning Studio
- b. Azure Databricks
- c. Azure Machine Learning Service
- d. Azure Data Factory
Answer: c. Azure Machine Learning Service
Explanation: Azure Machine Learning service offers model management and deployment services that easily integrate with existing Azure services.
True/False: The model’s performance cannot degrade over time once it is deployed.
- True
- False
Answer: False.
Explanation: A model’s performance can degrade over time due to changes in the data it processes, known as concept drift.
Which feature helps Azure automatically handle scaling of the deployed model service?
- a. Autoscaling
- b. AutoML
- c. Blue/Green Deployment
- d. Canary Release
Answer: a. Autoscaling
Explanation: Autoscaling allows Azure to automatically adjust the compute resources based on the load on the deployed model service.
True/False: All models must be retrained regularly after deployment.
- True
- False
Answer: False.
Explanation: Only models that have shown performance degradation or are processing different data need to be retrained.
What is the primary reason to use Azure Kubernetes Service (AKS) for deploying models in Azure ML?
- a. Cost Efficiency
- b. Scaling and high availability
- c. Both a and b
- d. None of the above
Answer: b. Scaling and high availability
Explanation: While Azure Containers can also help to reduce costs, their primary use in an ML application is for scaling and providing high availability.
True/False: When deploying multiple models as a pipeline in Azure, each model needs to be deployed individually.
- True
- False
Answer: False
Explanation: Models in a pipeline can be deployed collectively, allowing all the models in the pipeline to be used together in making predictions.
What component of Azure ML helps manage the lifecycle of a machine learning model?
- a. Machine Learning Model
- b. Machine Learning Workspace
- c. Experimentation Service
- d. Model Management Service
Answer: d. Model Management Service
Explanation: Model Management Service in Azure ML helps manage and track models throughout their lifecycles.
True/False: Deploying a machine learning model in Azure means making it available for real-time prediction.
- True
- False
Answer: True
Explanation: Deploying an ML model in Azure will mean the ML model can now provide real-time predictions through a web service.
Which language SDKs does Azure ML support for deploying models?
- a. Python SDK
- b. R SDK
- c. Both Python and R SDKs
- d. None of the above.
Answer: c. Both Python and R SDKs
Explanation: Azure ML provides support for both Python and R SDKs for deploying machine learning models.
True/False: Deploying a model in Azure requires a trained and validated model, the scoring script to run the model, and the environment needed for the model.
- True
- False
Answer: True.
Explanation: The model, scoring script, and environment are necessary components for deploying a machine learning model in Azure.
Interview Questions
What is Model Deployment in Azure?
Model deployment in Azure is the process of making a machine learning model available in a given production or testing environment from where the users or systems can consume its predictions.
How is the model deployment process done?
The model deployment process involves saving the trained model to a file, creating a scoring script, and specifying environmental dependencies. It also involves testing the deployment process on a local Docker container, and monitoring and diagnosing the deployed model using Application Insights.
What is Azure Machine Learning Service’s role in model deployment?
Azure Machine Learning Service simplifies the model deployment process. It creates a Docker image of the model, which can then be deployed to either Azure Container Instances, Azure Kubernetes Service, or Field-programmable gate arrays.
What is the significance of a scoring script in model deployment?
A scoring script is crucial in model deployment. It receives input data through a REST call, applies the model to the data, and returns the prediction results. It essentially governs how the model is used.
What are the key requirements for model deployment on Azure?
The key requirements for model deployment on Azure include a trained machine learning model, a scoring file or script to control how the model is used, and a defined environment with all necessary dependencies.
What is the role of Azure Container Instances (ACI) in model deployment?
Azure Container Instances (ACI) provides an environment for testing deployments. It’s used for small workloads and quick, iterative testing of machine learning models before they can be deployed in larger, scalable production settings.
What is Azure Kubernetes Service (AKS) used for in the model deployment process?
Azure Kubernetes Service (AKS) provides enterprise-grade security and governance for deploying models. It is used for high-scale production deployments, providing elastic capabilities to handle bursts of prediction requests.
How can models be consumed after they are deployed in Azure?
Deployed models in Azure can be consumed by sending HTTP POST requests to the web service endpoints.
Why would you use Application Insights for your deployed models in Azure?
Application Insights is used for monitoring the usage and performance of the models. It provides insights about how the model is being used, error logs, and custom events.
What should be contained in a model’s environment when deploying to Azure?
The model’s environment should contain all the Python packages required to run the model and the scoring script.
What are FPGA deployments and when are they used in Azure Model Deployment?
Field-programmable gate arrays (FPGAs) are used for specific types of workloads that require ultra-low latency. They are reconfigurable and designed to execute complex machine learning models efficiently.
How does Azure Machine Learning supports the Model deployment?
Azure Machine Learning supports model deployment by automatically creating a RESTful web service during deployment. This web service can receive data, apply the model to the data, and return predicted results.
What do you need to deploy a machine learning model with Azure Machine Learning?
To deploy a model with Azure Machine Learning, you need a trained machine learning model saved to a file, a scoring file, a dependencies file specifying software and hardware dependencies, and a deployment configuration file.
Is it necessary to retrain a model before deployment?
Yes, it is necessary to retrain the model with the full dataset before deployment to ensure best performance.
What is the role of Docker containers in Model deployment?
Docker containers encapsulate the model, scoring script, and dependencies. They ensure that the model operates in the same way regardless of the environment in which it is deployed.
extutorials.com