Module 02

MLflow-Based End-to-End ML Pipeline for Customer Churn

Designed a modular customer churn pipeline that separates data engineering, training, and inference while preserving unified experiment and artifact tracking. The system captures transformation lineage, model outputs, and real-time inference signals to support reliable iteration and production-style monitoring.

Impact

Enabled fully traceable experimentation with reproducible outputs, stronger model observability, and end-to-end monitoring from raw data to live prediction batches.

Focus

ML pipeline engineering, lifecycle observability, MLflow artifact tracking, reproducibility, and streaming inference monitoring.

Highlights

Stage-wise data lineage tracking from ingestion to scaled training datasets.
Structured model training and evaluation with automated MLflow logging.
Streaming inference with batch-level analytics for latency and risk trends.
Comprehensive artifact versioning for reproducible ML experiments.

Tech Stack

PythonPySparkMLflowXGBoostStreaming Inference

MLflow-Based End-to-End ML Pipeline for Customer Churn

A fully traceable ML lifecycle system spanning data engineering, model training, and real-time inference observability.

Problem

Traditional ML pipelines often lack proper tracking, making it difficult to reproduce experiments, trace data transformations, and monitor model performance in production.

Build full visibility from raw data ingestion to real-time inference.
Preserve reproducibility, lineage, and monitoring across all stages.

Approach

Built a modular pipeline that separates data processing, model training, and inference into independent stages while maintaining unified tracking through MLflow.

Designed stage-wise data processing with PySpark.
Implemented structured model training and evaluation workflows.
Integrated MLflow for experiment tracking and artifact management.
Developed a streaming inference pipeline with real-time logging.

Data Pipeline Engineering

Stage-wise transformations: raw -> cleaned -> encoded -> scaled.
Missing value handling and outlier detection using configurable strategies.
Feature engineering including binning, encoding, and scaling.
Train-test splitting with structured dataset outputs.
Logged row counts, missing values, and transformation metadata for lineage tracking.

Training Pipeline

Model training with XGBoost using configurable parameters.
Automated logging of metrics, parameters, and model artifacts.
Generated confusion matrix, ROC curve, and feature importance visualizations.
Versioned and stored outputs in MLflow for reproducibility.

Inference Pipeline

Streaming inference system with batch-based tracking.
Logged prediction metadata, inference time, and confidence scores.
Batch analytics for prediction distribution, risk categorization, and latency monitoring.
Enabled production-style monitoring of model behavior over time.

MLflow Tracking

Dataset versioning and lineage tracking.
Artifact logging for datasets, models, and visualizations.
Experiment tracking with parameters, metrics, and metadata.
Run-level reproducibility with complete artifact storage.

Architecture

PySpark-based data processing pipeline.
Modular training and evaluation layer.
MLflow tracking and artifact storage.
Streaming inference with batch logging and monitoring.

Challenges

Ensuring full reproducibility across multiple pipeline stages.
Managing large-scale data processing with efficient transformations.
Designing structured artifact tracking without excessive overhead.
Monitoring inference performance in real time.

Results

Built a fully traceable ML pipeline with end-to-end lifecycle visibility.
Enabled reproducible experiments with complete artifact tracking.
Improved observability through structured MLflow logging.
Implemented real-time inference monitoring with batch analytics.