Production ML Engineering: Packaging, APIs, and Testing

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Paid Certificate Available
Duration & workload

11 hours 20 minutes
Sessions

On-Demand
Level

Intermediate
Subtitles

English

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Overview

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Production ML Engineering: Packaging, APIs, and Testing focuses on transforming machine learning models into reliable production systems. In this course, you will learn how to package, deploy, document, and test machine learning applications so they can operate reliably in real-world environments. You will begin by creating reusable Python packages that organize machine learning code into maintainable modules. Next, you will learn how to build production-ready machine learning APIs that allow models to be accessed by applications and services. The course also introduces best practices for code review, version control, and CI/CD workflows used in modern ML engineering. As the course progresses, you will develop technical documentation that explains model architectures, training workflows, and API usage to support collaboration across teams. Finally, you will design automated testing strategies that validate machine learning pipelines and ensure reliable model outputs. By the end of the course, you will be able to package machine learning systems, deploy ML APIs, document AI systems, and implement automated testing workflows for production environments. Tools used in this course include Python, API frameworks, CI/CD pipelines, automated testing tools, and MLOps workflows.

Syllabus

Build Testable Python Packages for AI: Apply advanced programming constructs to build reusable utilities

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.

Build Testable Python Packages for AI: Create testable, standards-compliant Python packages for ML applications

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.

Develop Production-Ready ML APIs with MLOps: Maintaining ML Code Quality with Version Control and CI/CD

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.

Develop Production-Ready ML APIs with MLOps: Designing Modular ML APIs for Model Serving

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.

Document AI: Project & API Writing: Documenting Models, Data, and Training Pipelines

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.

Document AI: Project & API Writing: Writing Developer-Facing Docs for APIs and System Integration

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.

Automate and Evaluate ML Pipeline Tests: Designing Effective Test Cases for ML Pipelines

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

Automate and Evaluate ML Pipeline Tests: Automating Regression Tests for Stable Model Outputs

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.

Project: Package, Test, and Serve a Churn Prediction API

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.