Image: https://sagemaker-examples.readthedocs.io/en/latest/index.html

 

When we are talking about the data-driven value of Machine Learning systems, customer analysis is always drawing everyone’s interest. There is huge churn in the mobile industry due to the large volume of customers and various service providers.

Mobile operators have historical records on which customers ultimately ended up churning and which continued using the service. Leaving customers affect the business revenue badly. Hence mobile company wants to predict such customers in advance so that they can be retained with additional discount/offers etc.

Solution

I am going to build an XGBOOST model with the customer dataset. The data is the historical data. The dataset has the customer behaviour, transaction details which will help the algorithm to detect the pattern and predict the customer churn. The predicted customer is detected as “Churn” and “Not churn”. We will send the prediction to the client system via AWS API Gateway for further use by the business operation team. The operation team will use this data along with other CRM data and try to retain the customer whose churn prediction is high.

Objective

• Build a machine learning model using Sagemaker-XGBOOST-container offered by AWS Machine Learning Service.
• Deploy the Customer Churn model using the Sagemaker endpoint so that it can be integrated using AWS API gateway with the organization’s CRM system.

Approach

The solution will be implemented using AWS Sagemaker-XGBOOST-Container from the Notebook instance. Then the endpoint will be invoked by the Lambda function. AWS API Gateway will call the Lambda when the client system will send a POST request with test data to detect the churn or not churn.

Here we will use a public dataset churn.txt which is available in the Sagemaker sample data folder. the folder is accessible from the Sagemaker notebook instance as described below.

Image created by the author

 

Image: https://aws.amazon.com/blogs/machine-learning/call-an-amazon-sagemaker-model-endpoint-using-amazon-api-gateway-and-aws-lambda/

Create Sagemaker Notebook Instance

Start the notebook instance from the Sagemaker console.

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

 

Create the notebook instance with the default setup. Make sure to use the free tier instance (ml.t2.medium) to avoid the accidental costs for the demo project.

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

Image: Created by author

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

 

Then open the notebook instance by clicking on jupyter library. In Jupyter, choose New and then choose conda_python3.

For demo purposes, I am using the customer churn notebook available in the Sagemaker example. The code details are given below. You can use the same code to perform more data processing, apply different approaches and compare the various results.

However, here the focus is to build the inference endpoint using serverless architecture.

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

Prepare Data

Import the sagemaker libraries, create the sagemaker session instance. boto3 is imported to use the other AWS services in the notebook instance.

The demo s3 bucket is set to default sagemaker session bucket. The folder inside the bucket is set by a prefix which will store the train & validation datasets.

import sagemaker
sess = sagemaker.Session()
bucket = sess.default_bucket()
prefix = "sagemaker/DEMO-xgboost-churn"
# Define IAM role
import boto3
import re
from sagemaker import get_execution_role
role = get_execution_role()

Import other libraries.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import io
import os
import sys
import time
import json
from IPython.display import display
from time import strftime, gmtime
from sagemaker.inputs import TrainingInput
from sagemaker.serializers import CSVSerialize

The curn.txt file is available in Sagemker demo folder and can be copied using the below command in the notebook.

!aws s3 cp s3://sagemaker-sample-files/datasets/tabular/synthetic/churn.txt ./

output->>

download: s3://sagemaker-sample-files/datasets/tabular/synthetic/churn.txt to ./churn.txt

Read the file

churn = pd.read_csv("./churn.txt")
pd.set_option("display.max_columns", 500)
churn

churn.columns

Let’s begin exploring the data:

Frequency tables for each categorical feature

for column in churn.select_dtypes(include=["object"]).columns: display(pd.crosstab(index=churn[column], columns="% observations", normalize="columns"))
# Histograms for each numeric features
display(churn.describe())
%matplotlib inline
hist = churn.hist(bins=30, sharey=True, figsize=(10, 10))

Sample output is shown above, it will show all the columns in the above format.

churn = churn.drop("Phone", axis=1)
churn["Area Code"] = churn["Area Code"].astype(object)

for column in churn.select_dtypes(include=[“object”]).columns:

 if column != "Churn?": display(pd.crosstab(index=churn[column], columns=churn["Churn?"], normalize="columns"))
for column in churn.select_dtypes(exclude=["object"]).columns: print(column) hist = churn[[column, "Churn?"]].hist(by="Churn?", bins=30) plt.show()

The above screenshots are samples from the output, scroll down on the output section to check all the graphical visuals.

display(churn.corr())
pd.plotting.scatter_matrix(churn, figsize=(12, 12))
plt.show()

Let’s analyze the data and take decisions on feature extraction:

churn = churn.drop(["Day Charge", "Eve Charge", "Night Charge", "Intl Charge"], axis=1)

Applying dummies as one-hot encoding to convert categorical features to numeric.

model_data = pd.get_dummies(churn)
model_data = pd.concat( [model_data["Churn?_True."], model_data.drop(["Churn?_False.", "Churn?_True."], axis=1)], axis=1
)

Train the Model

We will use the Amazon SageMaker pre-built XGBoost container to train the model. This is AWS-managed, distributed scalable environment. Once the model is built we will host the model as a real-time prediction endpoint. The model artifacts will be saved in S3.

XGBoost is an efficient algorithm to handle non-linear relationships between features and the target variable.

Training data will be in either a CSV or LibSVM format for SageMaker XGBoost. We are using CSV format. It must have the predictor variable in the first column & will not have a header row.

Specify the locations of the XGBoost algorithm containers.

train_data, validation_data, test_data = np.split( model_data.sample(frac=1, random_state=1729), [int(0.7 * len(model_data)), int(0.9 * len(model_data))],
)
train_data.to_csv("train.csv", header=False, index=False)
validation_data.to_csv("validation.csv", header=False, index=False)

 

test_data.to_csv("test.csv", header=False, index=False)

len(train_data.columns)

 

boto3.Session().resource("s3").Bucket(bucket).Object( os.path.join(prefix, "train/train.csv")
).upload_file("train.csv")
boto3.Session().resource("s3").Bucket(bucket).Object( os.path.join(prefix, "validation/validation.csv")
).upload_file("validation.csv")

 

container = sagemaker.image_uris.retrieve("xgboost", boto3.Session().region_name, "latest")
display(container)

Training data is saved in S3. The validation data is used to evaluate the model. test data is used for final prediction. You can test the lambda and API using either validation or test data which are saved in the respective s3 folder as shown below. Also, train and validation csv files are saved in the sagemaker notebook instance folder.

s3_input_train = TrainingInput(

 s3_data="s3://{}/{}/train".format(bucket, prefix), content_type="csv"
)
s3_input_validation = TrainingInput( s3_data="s3://{}/{}/validation/".format(bucket, prefix), content_type="csv"
)
print_train_data = "s3://{}/{}/train".format(bucket, prefix)
print_test_data = "s3://{}/{}/validation/".format(bucket, prefix)
print(print_train_data)
print(print_test_data)

XGBoost hyperparameters are used to build the optimized models.

sess = sagemaker.Session()

xgb = sagemaker.estimator.Estimator(

container,

role,

instance_count=1,

instance_type=”ml.m4.xlarge”,
output_path=”s3://{}/{}/output”.format(bucket, prefix),
sagemaker_session=sess,
)
xgb.set_hyperparameters(
max_depth=5,
eta=0.2,
gamma=4,
min_child_weight=6,
subsample=0.8,
silent=0,
objective=”binary:logistic”,
num_round=100,
)
xgb.fit({“train”: s3_input_train, “validation”: s3_input_validation})

.

 

 

Deploy the Model

This code deploys the model on a server and creates a SageMaker endpoint that we can access. This step may take a few minutes to complete. Once we have a hosted endpoint running, we can make real-time predictions from the model simply by making an HTTP POST request via AWS API Gateway which will invoke the lambda, and lambda will send a request to the endpoint and get the response back from the endpoint.

xgb_predictor = xgb.deploy( initial_instance_count=1, instance_type="ml.m4.xlarge", serializer=CSVSerializer()
)

Create the Model Endpoint

Now the below code will deploy the model on an instance and create the SageMaker endpoint. Let’s wait for the endpoint to come “InService”. Then we can make real-time predictions if we pass the test data by making an HTTP POST request via AWS API Gateway. API will invoke the lambda function. Lambda will send a request to the endpoint and get the response back from the endpoint.

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

 

Create Lambda Function

Image: created by the author

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

 

I selected the new execution role for this lambda function. Once created, choose the Lambda AWS Identity and Access Management (IAM) role and add the following policy, which gives the function permission to invoke a model endpoint:

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

 

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

 

The new policy is shown below for reference, it can be pasted directly in attach policy screen.

{ "Version": "2012-10-17", "Statement": [ { "Sid": "VisualEditor0", "Effect": "Allow", "Action": "sagemaker:InvokeEndpoint", "Resource": "*" } ]
}

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

 

You can copy the Lambda Function from here 

import os

import io
import boto3
import json
import csv
print("Loading Lambda Function")
# grab environment variables
ENDPOINT_NAME = os.environ['ENDPOINT_NAME']
runtime = boto3.client("runtime.sagemaker")
def lambda_handler(event, context): # we simulate the data of a new call payload = event["payload"] # invoking the endpoint: make sure you substitute '' # with your endpoint name for example 'xgboost-2019-03-22-11-38-32-449' response = runtime.invoke_endpoint( EndpointName=ENDPOINT_NAME, ContentType="text/csv", Body=payload, ) print(response) result = json.loads(response["Body"].read().decode()) print(result) predicted_label = {"predicted_label": "Churn" if result > 0.80 else "Not Churn"} return predicted_label

ENDPOINT_NAME is an environment variable that stores the name of the SageMaker model endpoint, Add the new ENDPOINT_NAME variable on the “Edit environment variables” screen as shown below. Add value as the same name as the endpoint created by Customer Churn Model deployed in the above notebook.

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

 

Test the Lambda Function

Pass the below data to test the Lambda Function. The data format can be checked from the validation dataset which is saved in the S3  bucket as mentioned in the Notebook.

I downloaded the validation dataset, selected one record from the file, and saved it as CSV. Then opened the CSV with a notepad, removed the first column as it was the Target column. Copy this format and pasted in the Lambda function test section as shown below. For this function, the test data format is shown below.

{

“payload”: “62,0,5.072152061,5,6.600411338,2,3.533501079,300,4.395299899,7,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0”

}

This may vary from model to model. Basically, the test data format will be the same as the train data format which is used to train the model. The data parsing can be done in the lambda function also.

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

 

The result is showing a pass and returns the predicted value as “Not Churn” as returned by the endpoint. We can test with other records from the validation data set and will expect the predicted values as returned by the model endpoint.

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

Create Rest API

Create API steps:

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

 

1. On the API Gateway console, choose the REST API

2. Choose Build.

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

3. Select New API.

4. For API name¸ enter a name.

5. Leave Endpoint Type as Regional.

6. Choose Create API.

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

 

7. On the Actions menu, select Create a resource.

8. Enter a name for the resource -(eg PredictCustomerChurn)

9. After the resource is created, on the Actions menu, select Create Method to create a POST method.

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

 

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

10. For Integration type, select Lambda Function.

11. For Lambda function, enter the function created above.

12. Add permission to Lambda function – select OK

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

 

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

Deploy API

When the setup is complete, deploy the API to a stage.

13. On the Actions menu, Select Deploy API.

14. Create a new stage.

15. Select Deploy.

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

 

Select the correct Invoke API URL carefully.  The below invoke URL is correct as it contains both staging (eg-customerchurnAPIStage) and resource name (eg –predictcustomerchurn)

This URL is available on the Staging editor, click on the staging name link on the left pane (eg-customerChurnAPIStage), expand the staging name, expand the list to the resource, and then method – POST.  Click on POST and then the Invoke URL will be visible on the right panel with the correct URL.

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

 

Please note: The below invoke URL is incorrect – this is shown when the API is deployed and control goes to the staging editor. If we click on the staging name link on the left pane (eg-customerChurnAPIStage), the Invoke URL shown on the right-hand side area does not include the resource name.  Do not use this to copy invoke API URL.

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

 

Test API

Execute the test with the API Post method from the Method Execution Screen. Click on the TEST which will open the POST-method Test screen.

Pass the test data in the request body and click on test. The response will be displayed on the right-hand side panel. The Response Body will display the response value – eg “Churn” here.

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

 

Image: The screenshot is taken from the author’s own AWS account where the project has been implemented. Account information is hidden for security purposes.

 

As Lambda and API are now tested, let’s test the endpoint from outside the AWS cloud – I have selected the Postman application which is running on my laptop.

Test API using Postman

Here is the Postman setting, I passed the AWS API invoke URL on the right-hand side panel -> POST tab.

Then paste the same test data in the same JSON format with the “payload” variable in the  Test “Body”. then click “Send”.

The response will be received from Sagemaker model endpoint via AWS API and is shown on the bottom of the Postman screen in the response section.

Two test examples are shown here – one with Response =”Churn” and another one with “Not Churn”.

Image: The screenshot is taken from the author’s local postman setup.

 

Image: The screenshot is taken from the author’s local postman setup.

 

Clean up

Reference: https://aws.amazon.com/blogs/machine-learning/call-an-amazon-sagemaker-model-endpoint-using-amazon-api-gateway-and-aws-lambda/

Conclusion

1. https://aws.amazon.com/blogs/machine-learning/call-an-amazon-sagemaker-model-endpoint-using-amazon-api-gateway-and-aws-lambda/

2. https://aws.amazon.com/getting-started/hands-on/build-train-deploy-machine-learning-model-sagemaker/

About Author

Sarbani Maiti – Senior Consultant – Cloud Service | Machine Learning | MLOps| NLP | AWS Community Builder| Mentor. Helping businesses to leverage Cloud & AI/ML to build Data-Driven, Production-ready solutions. Love to discuss Data, ML, NLP, MLOPS.

https://www.linkedin.com/in/sarbani-maiti-35b89111/

The media shown in this article is not owned by Analytics Vidhya and are used at the Author’s discretion.

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• Source: https://www.analyticsvidhya.com/blog/2022/02/building-ml-model-in-aws-sagemaker/