You will apply advanced programming constructs such as generators, decorators, and structured logging to build reusable utilities for machine learning workflows. You will refactor preprocessing logic into modular components that improve maintainability.

You will create testable, standards-compliant Python packages for machine learning applications. You will structure dependencies, implement unit tests, and prepare packages for integration into production ML pipelines.

You will apply version control, code review workflows, and CI/CD pipelines to maintain ML codebase quality. You will implement automated checks that support collaboration and production readiness.

You will create modular software components and APIs for serving machine learning models. You will design and implement a structured service interface that supports scalable model deployment.

You will apply clear writing practices to document model architectures, data schemas, training procedures, and evaluation results. You will structure documentation to improve reproducibility and technical clarity.

You will create developer-facing documentation that defines request and response schemas, usage examples, and integration guidance. You will produce structured documentation that supports onboarding and long-term system maintenance.

You will evaluate an ML pipeline by designing comprehensive test cases that cover unit, integration, and smoke testing scenarios. You will define validation strategies that detect drift and performance degradation

You will create automated regression test suites to validate model outputs against baseline datasets. You will configure repeatable testing workflows that support stable and reliable model deployment.

In this project, you will transform churn prediction logic into a production-style machine learning service that is organized, testable, and easier for other developers to use. You will simulate the work of a machine learning engineer supporting a product analytics team that wants to operationalize churn-risk predictions for internal applications. Instead of delivering a single experimental script, you will structure prediction logic into reusable Python modules, implement automated tests to validate system behavior, and document how the prediction service should be used. Instead of delivering a single script, you will: Organize prediction logic into reusable modules Define a clear service interface Implement input validation and error handling Create automated tests Implement at least two advanced Python practices (e.g., structured logging, decorators, generators, configuration- driven design) Document how the system works, including model logic, data understanding, and evaluation results The final deliverable demonstrates how machine learning functionality can be packaged into structured code that other applications can depend on. Your completed project will represent a small but realistic machine learning service that can generate churn predictions from user engagement data. The final artifact is a portfolio-ready engineering project that reflects common machine learning operationalization work in professional environments.