MLOps Ep.4 Productionizing Evaluation and Deployment Script

mlops-evaluate.py

import numpy as np
import matplotlib.pyplot as plt
import azureml.core
# Display the core SDK version number
print("Azure ML SDK Version: ", azureml.core.VERSION)

# Define an environment
from azureml.core.environment import Environment
from azureml.core.conda_dependencies import CondaDependencies

# to install required packages
env = Environment('sklearn-usedcars-env')
cd = CondaDependencies.create(pip_packages=['azureml-dataprep[pandas,fuse]>=1.1.14', 'azureml-defaults'], conda_packages = ['scikit-learn==0.22.1'])

env.python.conda_dependencies = cd

# Register environment to re-use later
env.register(workspace = ws)

# Create a deployment configuration file
from azureml.core.webservice import AciWebservice
aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, 
                                               memory_gb=1, 
                                               tags={"data": "usedcars",  "method" : "sklearn"}, 
                                               description='Predict the price of used cars with sklearn regressors')

# Deploy in ACI
%%time
from azureml.core.webservice import Webservice
from azureml.core.model import InferenceConfig
from azureml.core.environment import Environment
from azureml.core import Workspace
from azureml.core.model import Model

ws = Workspace.from_config()
model = Model(ws, 'sklearn_regression_model')

myenv = Environment.get(workspace=ws, name="sklearn-usedcars-env", version="1")
inference_config = InferenceConfig(entry_script="score.py", environment=myenv)

service = Model.deploy(workspace=ws, 
                       name='sklearn-usedcars-mlprod', 
                       models = [model], 
                       inference_config = inference_config, 
                       deployment_config = aciconfig)

service.wait_for_deployment(show_output=True)