It’s essential to choose an appropriate framework to package a model for ensuring accurate execution and long-term scalability. More particularly, this involves encapsulating the data preprocessing and model scoring in a framework that can be used in different scenarios while maintaining model accuracy.
One of the modern solutions to package a model is to use Azure Machine Learning. It provides capabilities to manage the whole lifecycle of model development, from data preparation to deployment. By default, Azure Machine Learning supports some popular libraries/frameworks like Scikit-learn, TensorFlow, PyTorch, and ONNX. However, it’s essential to remember that the ultimate framework choice boils down to your specific requirements and considerations.
Firstly, let’s review the key aspects of packaging a model with Azure Machine Learning:
- Model File: This is the serialized version of the machine learning model. In the context of Azure Machine Learning, the model file often contains not only the learned parameters but also the whole model definition.
- Dependencies: To run the model, we need a runtime environment with all the required dependencies. Azure Machine Learning automatically records the Python packages needed to run the model.
- Scoring Script: This is a Python script that contains functions to load the model and use it to make predictions. Azure Machine Learning requires this script to load the model and make predictions.
Here is an example of how you can package a model with Azure Machine Learning:
from azureml.core.model import Model
# Register the model
model = Model.register(workspace=ws, model_name='my_model', model_path='./model.pkl')
# Define the dependencies
myenv = CondaDependencies()
myenv.add_conda_package("scikit-learn")
# Write the environment to yaml file
env_file = 'env.yml'
with open(env_file,"w") as f:
f.write(myenv.serialize_to_string())
# Define the scoring script
score_file = 'score.py'
with open(score_file,"w") as f:
f.write("""
import json
import numpy as np
import os
import pickle
from sklearn.externals import joblib
from sklearn.linear_model import LogisticRegression
def init():
global model
# note here "azureml-models" is a must while "sklearn_regression_model.pkl" is the name of model
model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')
# deserialize the model file back into a sklearn model
model = joblib.load(model_path)
def run(raw_data):
# make prediction
data = np.array(json.loads(raw_data)['data'])
y_hat = model.predict(data)
return json.dumps(y_hat.tolist())
""")
# Package the model
package = Model.package(ws, [model], inference_config)
package.wait_for_creation(show_output=True)
However, the choice of a framework to package a model may heavily rely on some considerations such as:
- Model Complexity: Some models, such as deep learning models, can be quite complex and may have unique dependencies, thus needing specific packaging capabilities.
- Organizational Requirements: In some cases, the choice may be constrained by the organization’s rules and limitations. Certain organizations may have a set of approved frameworks to use.
- Scalability: Depending on the workload, it might be necessary to opt for a framework that offers better scalability options.
- Maintenance: A good packaging framework should promote maintainability, particularly for long-running projects.
These considerations can guide the choice of an appropriate framework to package your models effectively for your data science solutions on Azure. Understanding these concepts will aid you in passing the DP-100 Azure Data Science exam while providing the knowledge needed to effectively prepare and package your machine learning models for production scenarios.
Practice Test
True or False: Packaging a model allows it to be used in different environments and platforms.
- True
- False
Answer: True
Explanation: Packaging a model is a crucial step in the deployment process as it allows the model to be utilized and interpreted across various platforms and environments.
Which of the following are valid frameworks for packaging a model in Azure?
- a) Azure Functions
- b) Azure Machine Learning
- c) Azure Logic Apps
- d) Azure Kinect
Answer: a) Azure Functions, b) Azure Machine Learning, c) Azure Logic Apps
Explanation: Both Azure Functions, Azure Machine Learning, and Azure Logic Apps provide mechanisms to package and deploy a Machine Learning model to be used in various applications. Azure Kinect is used for spatial computing, not for packaging a model.
True or False: Azure Machine Learning Studio is used for packaging models.
- True
- False
Answer: False
Explanation: Azure Machine Learning Studio is an interface for developing predictive analytics models but it is not used for packaging models.
What is the standard format to package a machine learning model in Azure Machine Learning?
- .pkl
Answer: .pkl
Explanation: In Azure Machine Learning, the standard format for packaging models is .pkl which stands for Python’s pickle module.
True or False: Model packaging involves converting a developed model into a specific format that can be easily understood by machines.
- True
- False
Answer: True
Explanation: The key purpose of model packaging is to convert a developed model into a specific format compatible with different machines and environments.
Which of the following is not part of the model packaging process?
- a) wrapping the model
- b) saving the model
- c) visualizing the model
- d) packaging the model
Answer: c) visualizing the model
Explanation: Model visualization is part of model development and analysis, not model packaging.
What is the main purpose of packaging a model in Azure ML?
- To make models easily distributable and ready for deployment.
Answer: To make models easily distributable and ready for deployment.
Explanation: Packaging a model makes it easy to distribute and deploy the model in a variety of learning and inference scenarios.
True or False: The ONNX runtime provides a single set of interfaces across multiple programming languages, optimizing computational graph-based machine learning models that are trained in different frameworks.
- True
- False
Answer: True
Explanation: The Open Neural Network Exchange (ONNX) Runtime is designed to accelerate model inferencing and training across a wide range of machine learning frameworks and hardware.
Which of the following should be considered when packaging a model?
- a) Model format
- b) Prediction speed
- c) Scoring script
- d) Size of the model
Answer: a) Model format, c) Scoring script, d) Size of the model
Explanation: While packaging a model, it’s important to consider the model format, scoring script and model size. Prediction speed is independent of the packaging process.
True or False: After packaging a model in Azure, you don’t need to register the model.
- True
- False
Answer: False
Explanation: Model registration is an important step after model packaging in Azure as it allows for version tracking and model audit history.
Interview Questions
Which Azure product would be most appropriate to deploy as a model packaging framework for Data Science?
Azure Machine Learning is the appropriate product for model packaging in Azure.
What are the prerequisites for packaging a model in Azure Machine Learning?
Before packaging a model, you need to have an Azure subscription, an Azure Machine Learning workspace, and a machine learning model trained in Azure.
What is the main file required to package a model in Azure?
The main file required to package a model is the scoring script which is required for every Azure Machine Learning model.
What is the role of the Score.py file with respect to model packaging?
Score.py is a Python script that is used to manage how your model deals with inputs and outputs. It is essential for generating test queries when testing the deployed model.
What is the purpose of the Conda dependencies (.yml) file in Azure model packaging?
The Conda dependencies file is used to define all the Python dependencies required to run the model and the scoring script within the Azure environment.
Which command is used to package a model in Azure?
The Azure CLI command ‘az ml model package’ is used to package the model in Azure.
How can you test if your machine learning model is properly packaged in Azure?
You can use the ‘az ml model profile’ command to test the packaged model.
Why is the model schema (model.json) file important in model packaging?
The model schema file (model.json) defines the input data schema for the models and is necessary for generating the API documentation for the model.
What are the primary components included in the model package?
The primary components in the model package typically include the serialized model, the scoring script and Conda environment dependencies.
How can you download a model package once it’s created in Azure?
‘az ml model download’ command is used to download the model package from Azure after it’s created.
What is the purpose of the “register_model” function in Azure ML SDK?
This function is used to store and version your trained models in your Azure ML workspace.
What is the role of azureml-core package for packaging a model?
The azureml-core package contains the essential APIs and classes needed for using Azure ML in Python, including the ones for packaging the model.
What are “Inference Configuration” and “Deployment Configuration” in Azure ML SDK?
“Inference Configuration” specifies the model, scoring script, and environment to use for deployment. “Deployment Configuration” specifies the compute target and other details of the deployment.
Can we have multiple models in a single deployment in Azure Machine Learning?
Yes, multiple models can be included in a single deployment using the ‘models’ parameter while configuring the inference.
What are the possible deployment targets for a packaged model in Azure?
Packaged models can be deployed to various targets including Azure Kubernetes Service (AKS), Azure Container Instances (ACI), or Azure Machine Learning Compute Instances.
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