MLOps - Customer Churn Prediction

End-to-end MLOps pipeline for predicting and preventing customer churn.

MLOps Pipeline Overview

flowchart TD
    A[Data Ingestion] --> B[Data Validation]
    B --> C[Feature Engineering]
    C --> D[Model Training]
    D --> E[Model Evaluation]
    E --> F{Performance OK?}
    F -->|Yes| G[Model Registry]
    F -->|No| D
    G --> H[Model Deployment]
    H --> I[Monitoring]
    I --> J[Drift Detection]
    J --> K{Drift Detected?}
    K -->|Yes| L[Auto-Retrain]
    K -->|No| I
    L --> D

    style A fill:#e3f2fd
    style G fill:#c8e6c9
    style L fill:#ffccbc

Training Pipeline

flowchart LR
    subgraph Data
        A[Raw Data] --> B[Clean]
        B --> C[Transform]
    end

    subgraph Features
        C --> D[Feature Selection]
        D --> E[Scaling]
    end

    subgraph Model
        E --> F[Train]
        F --> G[Validate]
        G --> H[Save Model]
    end

    style H fill:#c8e6c9

Monitoring Dashboard

sequenceDiagram
    participant M as Model
    participant D as Dashboard
    participant A as Alerts
    participant O as Ops

    loop Real-time
        M-->>D: Predictions + Metrics
        D->>D: Update Charts
        D->>A: Check Thresholds
        A->>O: Alert if Breach
    end

Model Registry Flow

flowchart TD
    A[Training Complete] --> B{Meet Threshold?}
    B -->|Yes| C[Register Model]
    B -->|No| D[Log Failure]
    C --> E[Tag Version]
    E --> F[Add Metadata]
    F --> G[Stage for Deployment]

    style C fill:#c8e6c9
    style D fill:#ffcdd2

A/B Testing Framework

flowchart TD
    A[Incoming Request] --> B{Split Traffic}
    B -->|50%| C[Model A]
    B -->|50%| D[Model B]
    C --> E[Collect Metrics]
    D --> E
    E --> F{Compare Performance}
    F --> G{Model B Better?}
    G -->|Yes| H[Deploy Model B]
    G -->|No| I[Keep Model A]

    style H fill:#c8e6c9

Key Components

Component	Purpose
Model Registry	Store and version models
Monitoring	Track performance metrics
Drift Detection	Detect data/concept drift
Auto-Retrain	Trigger retraining when needed

Performance Baselines

Metric	Target	Critical
Accuracy	>85%	<80%
Precision	>80%	<75%
Recall	>82%	<78%
Latency	<500ms	>1000ms

Running the Pipeline

# Start monitoring
python mlops/monitoring_dashboard.py

# Run complete pipeline
python mlops/churn_pipeline.py

# Check model registry
python mlops/model_registry.py --list