1. AWS ML Solution: Building a Robust Churn Prediction System for Telecom Using ML and AWS DevOps

AWS ML Solution: Building a Robust Churn Prediction System for Telecom Using ML and DevOps

Implementing Customer Churn Prediction in a Telecom Company

Business Requirement

A telecom company wants to implement a customer churn prediction system to identify customers at risk of leaving and proactively engage them. By predicting churn, the company aims to increase customer retention, improve customer satisfaction, and boost revenue. The solution involves collecting and preparing customer data, training a machine learning model to predict churn, deploying the model, and continuously monitoring its performance.

Roles Involved

• Data Engineer
• ML Developer
• Data Scientist
• MLOps Engineer
• DevOps Engineer

Step 1: Data Collection and Preparation

Role: Data Engineer

Responsibilities:

• Gathering and integrating data from various sources.
• Cleaning the data, handling missing values, and ensuring data quality.
• Building and maintaining data pipelines to automate data collection and preprocessing.

Tasks:

1. Data Collection and Preparation

Business/User Story: The marketing team collects data on customer demographics, usage patterns, and service feedback. We'll create three datasets with 2000 unique records each using synthetic data for this example.

Example Code:

python

import pandas as pd
import numpy as np

# Creating synthetic datasets
def create_synthetic_data(num_records):
    np.random.seed(42)
    data = pd.DataFrame({
        'customer_id': range(1, num_records + 1),
        'gender': np.random.choice(['Male', 'Female'], num_records),
        'age': np.random.randint(18, 70, num_records),
        'tenure': np.random.randint(1, 72, num_records),
        'monthly_charges': np.random.uniform(20, 150, num_records),
        'total_charges': np.random.uniform(20, 10000, num_records),
        'contract': np.random.choice(['Month-to-Month', 'One year', 'Two year'], num_records),
        'churn': np.random.choice([0, 1], num_records)
    })
    return data

dataset1 = create_synthetic_data(2000)
dataset1.to_csv("customer_data1.csv", index=False)
        

Benefit: Provides clean, well-structured datasets suitable for model training.

Step 2: Using BERT for Feature Extraction

Role: ML Developer

Responsibilities:

• Preprocessing data specifically for machine learning tasks.
• Applying feature engineering techniques and using advanced tools like BERT.
• Training and evaluating machine learning models.
• Tracking experiments and logging metrics.

Tasks:

1. Using BERT for Feature Extraction

Business/User Story: The data science team uses BERT for extracting meaningful features from textual data in the datasets (e.g., service feedback).

Example Code:

python

import pandas as pd
from transformers import BertTokenizer, BertModel
import torch

# Load dataset
data = pd.read_csv("customer_data1.csv")

# Preprocessing
data = data.dropna()  # Remove missing values
features = data.drop("churn", axis=1)
labels = data["churn"]

# Initialize BERT
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')

# Function to get BERT embeddings
def get_bert_embeddings(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    outputs = model(**inputs)
    return outputs.last_hidden_state.mean(dim=1).detach().numpy()

# Extract BERT features (example for a textual column if present)
# Assuming 'service_feedback' is a column in the dataset
# data['bert_features'] = data['service_feedback'].apply(get_bert_embeddings)

# For this example, we'll use numerical features directly
X = features[['age', 'tenure', 'monthly_charges', 'total_charges']]
y = labels
        

Benefit: Provides rich, high-dimensional feature representations using BERT.

Step 3: Experiment Tracking with MLflow

Role: ML Developer

Responsibilities:

• Training and evaluating machine learning models.
• Tracking experiments and logging metrics.

Tasks:

1. Experiment Tracking with MLflow

Business/User Story: The data science team tracks all experiments, parameters, and metrics to ensure reproducibility and transparency in model training.

Example Code:

python

import mlflow
import mlflow.sklearn
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Start MLflow run
with mlflow.start_run():
    # Train a model
    model = RandomForestClassifier(n_estimators=100)
    model.fit(X_train, y_train)

    # Log model
    mlflow.sklearn.log_model(model, "random_forest_model")

    # Log parameters and metrics
    mlflow.log_param("n_estimators", 100)
    accuracy = accuracy_score(y_test, model.predict(X_test))
    mlflow.log_metric("accuracy", accuracy)
        

Benefit: Facilitates comparison and analysis of different models, reducing manual documentation effort.

Step 4: Model Registration

Role: MLOps Engineer

Responsibilities:

• Registering models for version control.

Tasks:

1. Model Registration

Business/User Story: The registered model is version-controlled and can be easily retrieved for deployment.

Example Code:

python

# Register the model
model_uri = "runs:/{}/random_forest_model".format(mlflow.active_run().info.run_id)
model_details = mlflow.register_model(model_uri, "CustomerChurnModel")
        

Benefit: Ensures model versioning and simplifies model management, reducing deployment risks.

Step 5: Model Deployment

Role: MLOps Engineer

Responsibilities:

• Deploying models to production environments.

Tasks:

1. Model Deployment

Business/User Story: The registered model is deployed to a production environment for real-time inference.

Example Code:

python

import mlflow.pyfunc

# Deploy the model
model_version = model_details.version
model_name = "CustomerChurnModel"
model = mlflow.pyfunc.load_model(model_name, version=model_version)

# Use the deployed model
predictions = model.predict(pd.DataFrame(X_test))
        

Benefit: Streamlines the deployment process and ensures consistency between development and production environments.

Step 6: Real-time Monitoring and Continuous Improvement

Role: MLOps Engineer

Responsibilities:

• Monitoring model performance and handling updates for model drift.

Tasks:

1. Real-time Monitoring and Continuous Improvement

Business/User Story: The marketing team monitors the deployed model's performance and updates it periodically to handle model drift.

Example Code:

python

import mlflow
from sklearn.metrics import accuracy_score

# Define a function to monitor model performance
def monitor_model():
    # Fetch the latest registered model
    model = mlflow.pyfunc.load_model(model_name, version=model_version)
    
    # Predict and log performance metrics
    new_predictions = model.predict(pd.DataFrame(X_test))
    new_accuracy = accuracy_score(y_test, new_predictions)
    mlflow.log_metric("new_accuracy", new_accuracy)
    return new_accuracy

# Periodically call monitor_model (e.g., using a scheduled job)
monitor_model()
        

Benefit: Ensures the model remains effective over time by identifying performance issues early.

Step 7: Infrastructure Setup with Terraform

Role: DevOps Engineer

Responsibilities:

• Setting up the infrastructure for data pipelines, model training, and deployment.
• Ensuring scalability, reliability, and security of the deployed models.
• Automating the provisioning of cloud resources using Infrastructure as Code (IaC).

Tasks:

1. Infrastructure Setup with Terraform

Business/User Story: The DevOps team sets up the necessary cloud infrastructure to support the machine learning lifecycle, ensuring scalability and reliability.

Example Code (Terraform):

hcl

# Define the provider
provider "aws" {
  region = "us-west-2"
}

# Define the S3 bucket for data storage
resource "aws_s3_bucket" "mlflow_bucket" {
  bucket = "mlflow-artifacts"
  acl    = "private"
}

# Define IAM role for S3 access
resource "aws_iam_role" "mlflow_role" {
  name = "mlflow-role"
  assume_role_policy = jsonencode({
    "Version" : "2012-10-17",
    "Statement" : [
      {
        "Effect" : "Allow",
        "Principal" : {
          "Service" : "ec2.amazonaws.com"
        },
        "Action" : "sts:AssumeRole"
      }
    ]
  })
}

resource "aws_iam_role_policy" "mlflow_policy" {
  name   = "mlflow-policy"
  role   = aws_iam_role.mlflow_role.id
  policy = jsonencode({
    "Version" : "2012-10-17",
    "Statement" : [
      {
        "Effect" : "Allow",
        "Action" : [
          "s3:PutObject",
          "s3:GetObject",
          "s3:ListBucket"
        ],
        "Resource" : [_{{{CITATION{{{_1{](https://github.com/zrzka/zrzka.dev/tree/e2f53b29955b172e11ca21672037b997e8ad0f0f/content%2Fpost%2F2016-10-20-aws-journey-ec2-container-service.md)[_{{{CITATION{{{_2{](https://github.com/SupsKods/Aug2019/tree/fdad1ce7e13d9ab22cfb0fe67da2482b4b2e9af9/cloudeng-cloud-custodian%2Ftest-docker%2Fcustodian_docker_build%2Fcustodian%2Flib%2Fpython3.6%2Fsite-packages%2Ftests%2Ftest_iam.py)[_{{{CITATION{{{_3{](https://github.com/cloudy-native/aws-managed-policies/tree/70da46aed481093650be4312ff50c93e7c7795eb/body.md)
        

DevOps IaC Code Specification

Provider Configuration:

• Purpose: Specify the cloud provider and region for resource provisioning.
• Example:

S3 Bucket for Data Storage:

• Purpose: Create an S3 bucket to store MLflow artifacts.
• Example:

IAM Role for S3 Access:

• Purpose: Define an IAM role with permissions to access the S3 bucket.
• Example:

EC2 Instance for Model Training:

• Purpose: Provision an EC2 instance to be used for training machine learning models.
• Example:

Security Group for EC2 Instances:

• Purpose: Define a security group to allow necessary traffic to and from EC2 instances.
• Example:

EC2 Instance for Running MLflow:

• Purpose: Provision an EC2 instance to run the MLflow tracking server.
• Example:

Detailed Steps and Specifications

1. Define the Provider:
2. Create an S3 Bucket:
3. Define IAM Role and Policies:
4. Provision EC2 Instances:
5. Define Security Group:
6. Provision MLflow Instance:

Conclusion

By implementing an end-to-end machine learning solution for customer churn prediction, a telecom company can effectively identify customers at risk of leaving and take proactive measures to retain them. This comprehensive approach not only improves customer satisfaction but also boosts the company's revenue by reducing churn rates.

Summary of Steps:

1. Data Collection and Preparation (Data Engineer):
2. Using BERT for Feature Extraction (ML Developer):
3. Experiment Tracking with MLflow (ML Developer):
4. Model Registration (MLOps Engineer):
5. Model Deployment (MLOps Engineer):
6. Real-time Monitoring and Continuous Improvement (MLOps Engineer):
7. Infrastructure Setup with Terraform (DevOps Engineer):

Roles and Responsibilities:

• Data Engineer: Data gathering, cleaning, and preprocessing.
• ML Developer: Data preparation, feature extraction, model training, and experiment tracking.
• Data Scientist: Defining data requirements, designing experiments, and selecting algorithms.
• MLOps Engineer: Model registration, deployment, and monitoring.
• DevOps Engineer: Infrastructure setup, CI/CD pipeline configuration, and ensuring scalability and security.

By clearly defining and assigning these tasks, each team member can focus on their expertise, ensuring a smooth and efficient machine learning lifecycle that leads to accurate and reliable customer churn predictions. This structured approach leverages advanced tools like BERT for feature extraction and MLflow for tracking and deployment, combined with robust infrastructure management using Terraform.