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Generative AI for Java and Spring Development

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IntermediateCourse

This hands-on, project-driven course gives you the skills to bring artificial intelligence (AI) to life using Java and the Spring Framework. In just 3 weeks, you’ll learn how to build intelligent apps that create text, images, or recommendations quickly and become the go-to developer employers are looking for.To start, you’ll set up your Java development environment and dive into the core principles of AI. You’ll then move straight to building hands-on experience using tools and techniques such as Deeplearning4j and Spring AI in practical projects.

Language

  • English

Topic

  • Artificial Intelligence

Skills You Will Learn

  • Application Deployment, Application Programming Interface (API), Natural Language Processing, Development Environment, Spring Framework

Offered By

  • SkillUpEdTech

Estimated Effort

  • 9 hours

Platform

  • Coursera

Last Update

  • June 30, 2025
About this Course
Hello, and welcome to the Generative AI for Java and Spring Development course. 

This course is designed for Java and Spring developers looking to expand their expertise with generative AI. You’ll gain the knowledge and skills to leverage Java and Spring to design, implement, test, and deploy AI applications. You will explore how generative AI can help you build better, more useful applications with the programming language you’re already familiar with.

This course is part of the Generative AI for Java and Spring Developers specialization, designed to provide you with the practical skills and knowledge to excel in program management.

Course Syllabus

Module 1: AI Fundamentals with Java
  • Lesson 0: Welcome to the Course
  • Lesson 1: Getting Started with AI in Java
  • Lesson 2: Introduction to Java AI Libraries and Implementation
  • Lesson 3:  Glossary and Graded Quiz 
Key Topics in Module 1
  • AI Fundamentals and Java
  • Java Environment Setup for AI
  • Java AI Libraries Overview
  • AI Algorithm Implementation
  • Neural Networks with Deeplearning4j
  • Sentiment Analysis Applications
  • Image Processing Basics
  • Image Recognition Implementation
Module 2: Spring AI Integration
  • Lesson 1:  SpringAI Integration
  • Lesson 2: Building AI Applications with Spring
  • Lesson 3:  Glossary and Graded Quiz  
Key Topics in Module 2:
  • Overview of Spring AI and its integration with the Spring ecosystem
  • Spring AI Architecture and Components
  • Spring AI's abstraction layers and design patterns
  • Configuring Spring AI projects
  • RESTful API design for AI services
  • Basic recommendation algorithms with Spring AI
  • Working with language models in Spring AI
  • Implementing text generation features
  • Testing Strategies for AI Applications
  • Common issues in Spring AI applications
  • Debugging tools and techniques
  • Deploying Spring AI Applications
Module 3: Final Project, Final Assessment, and Course Wrap-Up
  • Lesson 1: Final Project
  • Lesson 2: Final Graded Quiz and Course Wrap-Up

Prerequisites

This course is recommended for developers looking to level-up their development careers using generative AI. Basic Java programming and AI knowledge is required. We encourage you to complete a program similar to one of the following if you do not have Generative AI experience:

  1. Generative AI: Introduction and Applications
  2. Generative AI: Prompt Engineering Basics
  3. Generative AI: Elevate your Software Development Career

Learning Objectives

After completing this course, you will be able to:
  • Explain AI concepts and set up a Java dev environment optimized for developing AI-powered applications with Java.
  • Build AI application features using Java and implement neural networks using Deeplearning4j for NLP, image recognition, and recommendation tasks.
  • Design and integrate AI service layers, RESTful APIs, and components into Spring applications using best practices.
  • Test, debug, deploy, and manage Spring AI apps with proper configuration, logging, and error handling strategies.

Instructors

Ramanujam Srinivasan

Chief Architect

With over 24 years of experience, I have effectively led technology and architecture teams, driving digital transformation and business growth for Fortune 500 companies. My expertise includes managing strategic consulting engagements and implementing large-scale solutions that have achieved annual contract values between $10M and $300M, particularly in turnaround scenarios. As an engineering leader, I guide cross-functional teams in deploying distributed solutions across various domains, including Cloud, AI/ML, eCommerce, Microservices, DevSecOps, legacy modernization, performance optimization, Generative AI, and Machine Learning. I prioritize building exceptional technology teams by mentoring and inspiring top technical talent, fostering a culture of rapid innovation and collaboration. I serve as a trusted advisor, nurturing long-term relationships with C-Level executives and stakeholders to drive the implementation of strategic technology initiatives. My approach combines a big-picture perspective with attention to detail, providing hands-on guidance for creating highly scalable and efficient solutions. I assist in developing minimum viable products (MVPs), evaluating products, enabling developers, and advocating for open-source technologies. As an innovation catalyst, I lead the ideation and Go-to-Market strategies for intellectual property solutions that leverage cloud services, data analytics, and AI/ML to provide strategic advantages to enterprise customers. Additionally, I have authored successful technology courses on Coursera covering topics such as Microsoft security, front-end and back-end development, DevOps, and Generative AI, positively impacting over 70,000 learners.

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