Apply SOLID Design to Optimize Java ML

This course is part of Level Up: Java-Powered Machine Learning Specialization

Instructor: Scott Cosentino

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

Advanced level

Recommended experience

5 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Advanced level

Recommended experience

5 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

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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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.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

"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.

This course is designed for senior Java developers and architects with at least 6 months of hands-on experience in Java programming and basic knowledge of machine learning. If you're ready to tackle "God Classes" and spaghetti code, and want to optimize your Java ML projects with SOLID principles for scalability, maintainability, and flexibility, this course is for you. To get the most out of this course, learners should have at least 6 months of hands-on experience with Java programming, including a solid understanding of object-oriented programming (OOP) concepts such as classes, interfaces, and abstract classes. Additionally, a basic knowledge of machine learning (ML) concepts is essential to fully grasp how to apply SOLID principles to Java ML projects. By the end of this course, you’ve unlocked the power of SOLID principles to create flexible, scalable, and maintainable Java ML systems. You now have the tools to refactor messy code, design modular components, and evaluate trade-offs between performance and design. With your newfound expertise in SOLID, Maven, Gradle, and best practices, you're ready to build production-ready machine learning applications that can evolve with ease. Keep applying these principles, and your code will become more reliable and adaptable with every project. Best of luck as you continue to level up your skills in Java and machine learning!

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

4 videos Total 20 minutes

Welcome to Enhancing Java ML Design with SOLID Principles 3 minutes
Dissecting the "God Class" Problem 5 minutes
Single Responsibility Principle in Action 5 minutes
Open/Closed Principle: Planning for Change 7 minutes

2 readings Total 10 minutes

Welcome to the Course: Course Overview 5 minutes
The Cost of Bad Design 5 minutes

1 peer review Total 30 minutes

Hands-On-Learning: Refactor a Monolithic Class Using Single Responsibility Principle 30 minutes

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

3 videos Total 17 minutes

Interface Segregation Principle: Right-Sized Contracts 6 minutes
Maven for ML Projects: Dependency Harmony 6 minutes
Gradle Alternative: Modern Build Automation 6 minutes

1 reading Total 5 minutes

Build Tool Best Practices for ML 5 minutes

1 peer review Total 20 minutes

Hands-On-Learning: Leveraging Maven and Gradle to Install Dependencies for an ML Project 20 minutes

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

5 videos Total 25 minutes

When SOLID Meets Reality: Performance vs Principles 9 minutes
The Pragmatic Approach: Design Decision Framework 5 minutes
Basic Patterns: Factory Pattern 4 minutes
Basic Patterns: Strategy Pattern 4 minutes
Congratulations and Your SOLID Foundation 2 minutes

1 reading Total 5 minutes

Clean Architecture in Practice 5 minutes

1 assignment Total 20 minutes

Apply SOLID Design to Optimize Java ML 20 minutes

2 peer reviews Total 80 minutes

Hands-On-Learning: Balancing SOLID Design and Performance in High-Throughput ML Systems 20 minutes
Project: Adaptive Combat Strategist (ML Edition) 60 minutes

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Instructor

Scott Cosentino

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10 Courses 1,312 learners

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