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Artificial Intelligence

AI for Java Developers: Building Intelligent Applications

Learn to integrate machine learning, LLMs, and intelligent automation into real-world Java applications through hands-on projects using Spring AI, ONNX, and modern AI APIs.

Intermediate Online 12 weeks 48 hours

About this course

Course Overview

This program is designed for professional Java developers who want to extend their backend skills into the world of Artificial Intelligence. You will learn how to integrate machine learning models, large language models (LLMs), and intelligent automation directly into Java-based applications, using industry-standard frameworks and cloud AI services.

What You Will Learn

  • Fundamentals of machine learning and how they apply to backend engineering
  • Integrating LLM APIs (OpenAI, Azure OpenAI, local models) into Java services
  • Building AI-powered microservices with Spring AI and Spring Boot
  • Running inference with ONNX Runtime and deploying pre-trained models in the JVM
  • Designing retrieval-augmented generation (RAG) pipelines with vector databases
  • Applying AI responsibly: security, cost control, and monitoring in production

Who Should Enroll

This course is ideal for backend developers, software engineers, and technical leads who already work with Java and Spring and want to add practical AI engineering skills to their profile.

Hands-On Approach

Each module combines short theoretical sessions with practical labs, culminating in a capstone project where students build and deploy a complete AI-enhanced Java application.

What you'll learn

  • Integrate LLM-based APIs into Java and Spring Boot applications
  • Build and deploy AI-powered microservices using Spring AI
  • Run local and cloud-based model inference from the JVM
  • Design and implement retrieval-augmented generation (RAG) pipelines
  • Use vector databases to power semantic search features
  • Apply prompt engineering techniques within Java-based workflows
  • Monitor, secure, and optimize AI features in production environments
  • Deliver a complete AI-enhanced Java capstone project

Prerequisites

  • Solid experience with Java (Java 11+) and object-oriented programming
  • Familiarity with Spring Boot and REST API development
  • Basic understanding of relational or NoSQL databases
  • No prior machine learning experience required

Advantages

🤖

Java-first AI training

Unlike generic AI courses, every concept is taught through Java and Spring, so you apply skills directly to your existing stack.

🛠️

Hands-on labs every week

Each module includes practical exercises building real AI features, from LLM calls to RAG pipelines.

🚀

Capstone project

Finish the program with a deployable AI-enhanced Java application you can showcase to employers or clients.

👩‍🏫

Expert instructors

Learn from practicing engineers with real experience integrating AI into production Java systems.

🔒

Production-ready practices

Covers security, cost management, and monitoring so AI features are safe and reliable in real deployments.

Training program

  1. 1 Overview of AI, ML, and LLMs for engineers
  2. 2 Where AI fits into modern Java architectures
  3. 3 AI terminology and core concepts
  4. 4 Setting up the Java + AI development environment
  1. 1 Calling OpenAI and Azure OpenAI APIs from Java
  2. 2 Handling streaming responses and tokens
  3. 3 Error handling, retries, and rate limits
  4. 4 Building a simple AI chatbot service
  1. 1 Introduction to the Spring AI framework
  2. 2 Configuring AI clients and models
  3. 3 Structured prompts and output parsing
  4. 4 Building AI-powered REST endpoints
  1. 1 Principles of effective prompt design
  2. 2 Few-shot and chain-of-thought prompting
  3. 3 Managing context windows and token limits
  4. 4 Testing and evaluating prompt quality
  1. 1 Introduction to ONNX Runtime for Java
  2. 2 Loading and running pre-trained models
  3. 3 Performance considerations on the JVM
  4. 4 Choosing between cloud and local inference
  1. 1 Concepts of embeddings and vector search
  2. 2 Integrating vector databases (e.g., Pinecone, Weaviate)
  3. 3 Building a RAG pipeline in Spring Boot
  4. 4 Improving accuracy with chunking strategies
  1. 1 Designing scalable AI microservices
  2. 2 Caching and reducing API costs
  3. 3 Asynchronous processing for AI workloads
  4. 4 Combining multiple AI services in one system
  1. 1 Securing API keys and sensitive data
  2. 2 Logging and monitoring AI usage
  3. 3 Handling hallucinations and output validation
  4. 4 Cost tracking and budget controls
  1. 1 Defining the capstone AI application
  2. 2 Implementing core AI features end-to-end
  3. 3 Testing, deployment, and demo preparation
  4. 4 Final project presentation and review

FAQ

No, prior ML experience is not required. This course focuses on practical AI integration for developers who already know Java and Spring.

You will work with OpenAI and Azure OpenAI APIs, Spring AI, ONNX Runtime, and vector databases such as Pinecone or Weaviate.

Yes, each module includes hands-on labs, and the course ends with a capstone project where you build a complete AI-enhanced Java application.

The course runs for 12 weeks with a total of 48 hours, averaging about 4 hours per week including lectures and labs.

Yes, students who complete the course and capstone project receive an Ingress Academy certificate of completion.

Yes, Kotlin developers on the JVM can follow along easily, as most concepts and libraries are fully interoperable with Java.