"Tired of ""God Classes"" and spaghetti code in your Java ML projects? This course, ""Enhance Java ML Design with SOLID Principles,"" is for senior developers and architects ready to build resilient software. The secret to reliable systems is accepting that requirements always evolve. Master the S.O.L.I.D. principles to write code that embraces future changes with minimal impact.
Apply SOLID Design to Optimize Java ML
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Apply SOLID Design to Optimize Java ML
This course is part of Level Up: Java-Powered Machine Learning Specialization
Instructor: Scott Cosentino
Included with
Gain insight into a topic and learn the fundamentals.
Advanced level
Recommended experience
4 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Apply the Single Responsibility Principle (SRP) and Open/Closed Principle (OCP) to create modular and extensible components.
Implement the Liskov Substitution Principle (LSP) and the Dependency Inversion Principle (DIP) to build flexible and decoupled components.
Utilize Maven and Gradle to manage dependencies and structure a Java ML project.
Evaluate design trade-offs when applying SOLID principles to a Java ML project.
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Recently updated!
January 2026
Assessments
1 assignment
Taught in English
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This course is part of the Level Up: Java-Powered Machine Learning Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
In this module, learners will start with a messy "ModelHandler" class that violates multiple SOLID principles. Learners will learn to identify code smells, understand the business impact of poor design, and systematically refactor using SRP and OCP. By the end, learners will have transformed a monolithic class into a clean, modular system that's ready for future changes.
What's included
4 videos2 readings1 peer review
This lesson focuses on creating truly flexible ML systems. You'll learn how LSP ensures your abstractions work correctly with any implementation, while DIP helps you build systems that depend on abstractions rather than concrete implementations. We'll show how to swap out different ML models and data sources without breaking your application.
What's included
3 videos1 reading1 peer review
In this lesson, you'll learn to design clean, focused interfaces that don't force clients to depend on methods they don't use. We'll also dive into practical project management with Maven and Gradle, showing how proper build tool configuration supports clean architecture and manages the complex dependency trees common in ML projects.
What's included
3 videos1 reading1 peer review
This final lesson brings everything together by examining real-world scenarios where strict adherence to SOLID principles might conflict with practical concerns like performance, simplicity, or time constraints. You'll develop a framework for making pragmatic design decisions and learn when to prioritize certain principles over others.
What's included
5 videos1 reading1 assignment2 peer reviews
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