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SkillUp
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Ramanujam Srinivasan
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.
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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.
Read more
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 that employ powerful Java and Spring AI libraries. Plus, you’ll integrate your AI skills with Spring, leveraging its robust architecture to build smart applications.
This course is ideal for developers with a working knowledge of Java and Spring who want to break into the fast-moving world of AI without switching ecosystems or learning a new language from scratch.
If that’s you… enroll today and build the highly sought-after GenAI skills employers are actively seeking in just 3 weeks.
Register for this course and see more details by visiting:
OpenCourser.com/course/yseq9a/generative
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What's inside
Syllabus
AI Fundamentals with Java
This module introduces learners to the fundamentals of artificial intelligence using Java. You will learn the core concepts of AI, set up a Java development environment for AI applications, and gain hands-on experience with key Java AI libraries. Through practical labs and guided projects, you will build their first AI applications, implement natural language processing techniques, and develop basic image recognition systems. This module is designed for Java developers who want to explore AI implementation in Java.
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Syllabus
AI Fundamentals with Java
This module introduces learners to the fundamentals of artificial intelligence using Java. You will learn the core concepts of AI, set up a Java development environment for AI applications, and gain hands-on experience with key Java AI libraries. Through practical labs and guided projects, you will build their first AI applications, implement natural language processing techniques, and develop basic image recognition systems. This module is designed for Java developers who want to explore AI implementation in Java.
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Generative AI for Java and Spring Development.
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Career center
Learners who complete Generative AI for Java and Spring Development will develop knowledge and skills
that may be useful to these careers:
Generative Artificial Intelligence Engineer
Generative Artificial Intelligence Engineer
A Generative Artificial Intelligence Engineer specializes in developing and deploying AI models that can autonomously produce new content, such as text, images, or code. This course is a direct pathway to becoming a highly sought-after Generative Artificial Intelligence Engineer, providing hands-on experience in building intelligent applications using Java and the Spring Framework. You will learn to leverage powerful Java and Spring AI libraries to create innovative generative solutions, from initial setup of your Java AI development environment to implementing practical text and image generation systems. This course specifically addresses the employer demand for professionals who can bring AI to life within existing Java and Spring ecosystems, making you adept at building and deploying generative AI capabilities.
Spring Artificial Intelligence Developer
Spring Artificial Intelligence Developer
A Spring Artificial Intelligence Developer specializes in creating intelligent applications by integrating AI capabilities into the Spring Framework ecosystem. This course is tailor-made for becoming a proficient Spring Artificial Intelligence Developer, offering a deep dive into leveraging Spring's familiar programming model with AI. You will gain hands-on experience building, testing, and deploying AI applications using Spring AI, from setting up your development environment to implementing features like recommendations and text generation. This course directly addresses the need for developers who can seamlessly combine Spring's robust architecture with powerful AI libraries, enabling you to build highly sought-after intelligent applications efficiently.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer builds, trains, and deploys machine learning models and systems to solve complex problems. This course offers a robust entry point into the field of Machine Learning Engineering by focusing on practical application development with Java and Spring. You'll gain crucial skills in core AI principles and hands-on experience with tools like Deeplearning4j, essential for developing various intelligent applications, including recommendation systems. The course emphasizes integrating AI capabilities using Spring AI, allowing you to confidently build, test, and deploy AI-powered features. For developers aiming to transition into machine learning without altering their core technology stack, this course provides the perfect blend of AI fundamentals and practical implementation.
Intelligent Application Developer
Intelligent Application Developer
An Intelligent Application Developer specializes in creating software that incorporates artificial intelligence to perform smart functions like generating content or making recommendations. This course is designed to transform you into a skilled Intelligent Application Developer, providing the hands-on experience needed to build sophisticated AI-driven applications using Java and the Spring Framework. You will learn how to integrate powerful AI capabilities, from setting up your development environment and understanding core AI principles to implementing practical projects with Java AI libraries and Spring AI. By mastering how to build, test, and deploy intelligent applications using Spring's robust architecture, you will meet the demand for developers who can bring AI to life.
Artificial Intelligence Software Engineer
Artificial Intelligence Software Engineer
An Artificial Intelligence Software Engineer designs, develops, and maintains software solutions that incorporate artificial intelligence capabilities. This course is precisely tailored for individuals aspiring to excel as an Artificial Intelligence Software Engineer, equipping you with the expertise to seamlessly integrate AI into applications using your existing Java and Spring development knowledge. You will learn to set up an AI-ready Java environment, grasp core AI concepts, and implement practical AI applications such as natural language processing and image recognition. The focus on Spring AI integration ensures you can leverage Spring's robust architecture to build, test, and deploy intelligent applications efficiently, turning you into a developer employers are actively seeking for AI-driven projects.
Applied Machine Learning Engineer
Applied Machine Learning Engineer
An Applied Machine Learning Engineer focuses on translating theoretical machine learning models into practical, deployable applications that solve real-world problems. This course is highly beneficial for an Applied Machine Learning Engineer, providing hands-on experience in bringing artificial intelligence to life using Java and the Spring Framework. You will learn how to apply core AI principles, build intelligent apps that create text or recommendations, and integrate powerful Java AI libraries and Spring AI into robust systems. The project-driven approach ensures you gain the skills to confidently build, test, and deploy functional AI applications, making you proficient in practical machine learning implementation within a familiar development ecosystem.
Recommendation Systems Engineer
Recommendation Systems Engineer
A Recommendation Systems Engineer designs and implements algorithms and systems that suggest relevant items or information to users. The course directly prepares you for this role by offering specific modules on building recommendation systems using Spring AI. You will acquire hands-on experience in developing practical artificial intelligence powered features, learning to create intelligent applications that provide tailored suggestions. By leveraging your Java and Spring knowledge, you will integrate advanced AI capabilities into robust applications, mastering the deployment of Spring AI for creating impactful recommendation engines. This course is ideal for developers keen on enhancing user experiences by building sophisticated, personalized recommendation technologies.
Natural Language Processing Engineer
Natural Language Processing Engineer
A Natural Language Processing Engineer develops systems that allow computers to understand, interpret, and generate human language. This course directly contributes to building a foundation for this specialized role by introducing natural language processing techniques within a Java development environment. You will gain practical experience in implementing AI applications that create text and process linguistic data using Java AI libraries. The integration of Spring AI further enhances your ability to build sophisticated NLP-powered features, demonstrating how to leverage a familiar framework to develop, test, and deploy language-focused intelligent applications. This course is an excellent choice for Java and Spring developers eager to specialize in NLP without switching their preferred programming ecosystem.
Enterprise Artificial Intelligence Developer
Enterprise Artificial Intelligence Developer
An Enterprise Artificial Intelligence Developer builds and integrates AI solutions specifically for large organizational systems and business processes. Given that Java and Spring are widely used in enterprise environments, this course provides an excellent foundation for an Enterprise Artificial Intelligence Developer. You will learn to set up a Java development environment for AI applications and integrate AI capabilities using Spring AI, enabling you to build intelligent apps that create text, images, or recommendations within the robust Spring architecture. This course ensures you can leverage familiar enterprise technologies to deploy sophisticated AI solutions that enhance business operations and efficiency.
Artificial Intelligence Platform Developer
Artificial Intelligence Platform Developer
An Artificial Intelligence Platform Developer specializes in building and maintaining the foundational software and services that support AI model development, deployment, and scaling. This course provides excellent preparation for an Artificial Intelligence Platform Developer by focusing on robust integration of AI capabilities within the Spring Framework, a common choice for enterprise-level platforms. You will learn to set up a Java development environment for AI, grasp core AI concepts, and gain hands-on experience using Spring AI to build, test, and deploy intelligent applications. This practical skill set enables you to contribute to creating efficient and scalable AI platforms, leveraging Java and Spring's strengths for sophisticated AI solution delivery.
Computer Vision Engineer
Computer Vision Engineer
A Computer Vision Engineer develops systems that enable computers to interpret and understand visual information from the world. This course contributes to preparing you for this field by covering basic image recognition systems within a Java environment. You will gain practical experience in implementing AI applications that process images using Java AI libraries. The course’s project-driven approach, combined with the integration of Spring AI, allows you to build and deploy intelligent applications capable of visual interpretation. This expertise is valuable for developers who wish to apply their Java and Spring skills to the growing demands of computer vision and image processing without learning an entirely new ecosystem.
Artificial Intelligence Product Developer
Artificial Intelligence Product Developer
An Artificial Intelligence Product Developer is responsible for conceiving, building, and refining products that are powered by artificial intelligence. This course offers practical, project-driven experience directly applicable to the role of an Artificial Intelligence Product Developer. You will learn to bring AI to life using Java and Spring, developing intelligent apps that create text, images, or recommendations. By gaining hands-on experience with Spring AI integration, you will be equipped to build, test, and deploy AI-powered features within a robust framework. This allows you to rapidly prototype and develop new AI products effectively, leveraging your existing Java and Spring expertise to create highly sought-after solutions for employers.
Artificial Intelligence Integrator
Artificial Intelligence Integrator
An Artificial Intelligence Integrator specializes in embedding AI functionalities into existing software systems and platforms. This course is particularly relevant for an Artificial Intelligence Integrator, as its core focus is on integrating AI capabilities using the Spring Framework into Java applications. You will learn how to leverage Spring's familiar programming model to connect AI models and services seamlessly, building intelligent apps like recommendation systems or text generators. The hands-on experience with Spring AI ensures you can confidently take AI skills and apply them within a robust application architecture, making you adept at enhancing current systems with advanced artificial intelligence.
Java Backend Developer
Java Backend Developer
A Java Backend Developer builds and maintains the server-side logic and databases that power web applications. While primarily focused on artificial intelligence, this course significantly enhances the profile of a Java Backend Developer by adding cutting-edge AI integration skills. You will learn to set up an AI-ready Java development environment and integrate AI capabilities using the Spring Framework, a core technology in backend development. This means you can build intelligent applications that create text or recommendations directly within your existing Java and Spring ecosystem, making your backend solutions more powerful and intelligent. This course provides a forward-thinking way to advance your career by embedding AI into traditional backend roles.
Artificial Intelligence Solutions Architect
Artificial Intelligence Solutions Architect
An Artificial Intelligence Solutions Architect designs and oversees the implementation of complex artificial intelligence systems and strategies for organizations. While this role typically requires significant experience and often benefits from an advanced degree in a related field, this course may be helpful by providing a foundational understanding of how to build AI applications using Java and Spring. You will gain insight into AI fundamentals, practical implementation with Java AI libraries, and crucially, how to integrate AI capabilities into robust application architectures using Spring AI. This hands-on experience in building intelligent applications will help you understand the technical feasibility and implementation challenges of AI solutions, which is essential for making informed architectural decisions in future.
For more career information including salaries, visit:
OpenCourser.com/course/yseq9a/generative
Reading list
We've selected 21 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Generative AI for Java and Spring Development.
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Is the definitive guide for integrating generative AI capabilities directly into the Spring ecosystem. It provides deep dives into the Spring AI project, covering model orchestration, embeddings, and vector databases. It is highly valuable as a current reference for developers looking to implement the specific 'Spring AI Integration' module of the course.
Provides a foundational understanding of how to interact with Large Language Models (LLMs) via APIs, which core skill for this course. It covers prompt engineering and fine-tuning concepts that are directly applicable to the Spring AI framework. It serves as an excellent bridge between general AI theory and practical application development.
Practical guide for Java developers to implement various machine learning algorithms using popular libraries like Weka and MOA. It provides the necessary background knowledge for the 'AI Fundamentals' module of the course. It is frequently used by industry professionals to transition from standard Java development to data-driven application building.
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Adds significant depth to the course by explaining the mechanics of vector search and semantic retrieval, which are critical for RAG (Retrieval-Augmented Generation). It explains how to build intelligent recommendation systems, a key learning objective in the Spring AI module. It useful reference tool for developers building production-grade AI search features.
Is perfect for the 'AI Fundamentals with Java' module as it assumes no prior AI knowledge. It covers basic NLP and image recognition using Java, mirroring the course's early syllabus. It is more valuable as a beginner's primer than as a deep technical reference for experienced AI engineers.
Knowledge of Spring Boot strict prerequisite for this course, and this book is the industry standard for learning the framework. It ensures the learner understands the auto-configuration and dependency injection patterns that Spring AI relies upon. Reading this book first provides the necessary structural foundation to handle the course's complex AI integrations.
This very recent guide that focuses on the latest version of Spring Boot, which is the version most compatible with Spring AI. It provides a solid foundation for the 'Spring AI Integration' module by teaching the latest best practices in the framework. It is commonly used as a textbook for developers modernizing their skill set.
Provides a hands-on approach to implementing ML projects using Java, focusing on the software engineering aspects of AI. It aligns well with the project-driven nature of the SkillUp course syllabus. It is particularly helpful for setting up the Java development environment for AI, which is the first step in the course.
As the course mentions deploying AI applications, this book provides the context for running Spring applications in modern cloud environments like Kubernetes. It covers the resilience and scalability patterns required when calling external AI APIs. It is highly recommended for developers who want to move their final project from local development to a production cloud.
Provides a high-level architectural view of how to build reliable and scalable AI systems. It is essential for learners who want to understand the lifecycle of an AI project beyond just writing code. It adds breadth to the course by covering data engineering and model monitoring, which are crucial for professional development.
Focuses on the art of crafting prompts to get the best results from LLMs, a key component of building intelligent apps. It complements the 'Spring AI Integration' module by teaching learners how to structure the text inputs their Java code will send to models. It quick but essential read for maximizing the effectiveness of AI features.
Covers modern Java features like Streams and Lambdas which are extensively used in the Spring AI and Deeplearning4j libraries. It serves as prerequisite knowledge for developers who may have been working with older versions of Java. Mastering these concepts is necessary to follow the course's practical labs effectively.
A concise and highly-regarded guide to the professional practice of machine learning. It provides the 'best practices' that the course syllabus touches upon for building and testing AI applications. It is more valuable as additional reading for those looking to turn their course project into a professional-grade application.
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Since many Spring applications are deployed to AWS, this book provides specific context for using Amazon Bedrock with Spring AI. It covers the infrastructure side of generative AI that the course syllabus only briefly mentions. It great resource for the final project's deployment phase.
While LangChain is often associated with Python, this book explains the core patterns of LLM orchestration that the LangChain4j and Spring AI libraries emulate. Understanding these patterns allows Java developers to translate AI innovations from other ecosystems into their own. It is valuable as additional reading to understand the 'why' behind AI framework design.
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This comprehensive professional reference for the core Spring Framework 6, which introduced the foundational support for Spring AI. It provides the deep technical details of the framework's internals. It is best used as a reference tool for troubleshooting complex integration issues during the course's final project.
Covers the broader engineering challenges of AI, such as handling feedback loops and model drift. It adds significant breadth to the course by teaching learners how to think like an AI product engineer. It is commonly used by industry professionals to move beyond simple 'hello world' AI examples.
Offers a very practical, code-first approach to deploying deep learning models. It helps students understand how to take the models they work with in Deeplearning4j and deploy them effectively. It useful reference for the image recognition systems mentioned in the syllabus.
Modern AI applications are often built as microservices. teaches how to build the robust, distributed architecture that the course's AI components will likely live within. It provides background knowledge on service discovery and API gateways which are relevant for the final project's architecture.
Focusing specifically on the interaction with OpenAI's models, this book is highly relevant for the text generation parts of the course. It provides practical templates that can be used directly in the Spring AI labs. It quick reference tool for improving the quality of AI-generated recommendations.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/yseq9a/generative
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Effort
3 weeks at 2 to 3 hours a week
Level
Intermediate
Via
Coursera
Institution
SkillUp
Instructors
SkillUp
Ramanujam Srinivasan
Session
November 3, 2025
- December 1, 2025
Language
English
Transcript
Español
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