AI Integration for Java Developers Voeg toe aan favorieten

Introduction
Stay ahead in AI-driven software development. Learn how to use AI to accelerate your workflow and integrate intelligent features directly into Java applications.

Detailed description
AI is reshaping software development faster than any technology in recent memory. As a Java developer, you face a choice: adapt and thrive, or watch as others leverage AI to build better software faster. This training ensures you’re not just keeping pace. You’re leading the charge.

Led by Mauro Palsgraaf and Tom Wigleven, you’ll master both sides of the AI transformation in two comprehensive days: accelerating your development workflow with tools like GitHub Copilot, and embedding powerful AI capabilities directly into your applications. This dual approach means you’ll code faster AND build smarter applications. A combination that makes you invaluable in today’s market.

Day one transforms how you work. You’ll move beyond basic AI tool usage to master prompting techniques that can make you 2-3x more productive, learn when AI helps (and when it hinders), and discover strategies for code review, refactoring, and testing that leverage AI effectively. You’ll also establish the AI fundamentals needed for application integration, working hands-on with popular Java AI libraries. Day two unlocks enterprise-scale AI features. You’ll implement Retrieval Augmented Generation (RAG) systems, integrate cloud AI services, deploy local models, and master the performance optimization and cost management strategies that separate proof-of-concepts from production systems.

Every technique is demonstrated through practical examples and real-world scenarios from enterprise environments. Walk out ready to ship AI-enhanced features that differentiate your applications, accelerate your development workflow, and position you at the forefront of the AI-enabled development era.

Prerequisites
You’ll need solid Java development experience and familiarity with REST APIs and modern frameworks. Some experience with GitHub and modern IDEs helps for the AI-assisted development portions. No prior AI experience required. We’ll build that foundation together.

Target audience
Designed for Java developers, architects, and tech leads who recognize that AI is transforming both how we build software and what we can build. Whether you’re exploring AI capabilities, planning AI features for your applications, or leading teams that need to adopt AI tools, this training gives you practical expertise that’s immediately applicable.

Learning goals
You’ll gain comprehensive AI expertise:

• Understanding AI-assisted development capabilities and limitations
• GitHub Copilot setup, configuration, and effective usage patterns
• Prompt engineering fundamentals for development acceleration
• Code review and refactoring strategies with AI assistance
• Testing approaches in AI-assisted development workflows
• AI fundamentals tailored for Java developers
• Java AI libraries (DJL, Tribuo, Deeplearning4j) implementation
• Cloud AI service integration (AWS Bedrock, Azure OpenAI, Google AI)
• Local model deployment and management with Java
• RAG (Retrieval Augmented Generation) application development
• Embedding LLMs into existing enterprise applications
• Performance optimization and cost management strategies
• Testing and validation approaches for AI components
• Production deployment patterns for AI-enhanced Java applications
• Ethical considerations and best practices for AI development

Topics covered
Master the complete AI toolkit:

• Proficiency with AI-assisted development tools for accelerated coding
• Understanding of effective prompting techniques for development tasks
• Experience with troubleshooting and optimizing AI development tools
• Understanding of AI capabilities and limitations in Java applications
• Proficiency with Java AI libraries and frameworks
• Experience with both cloud and local AI model integration
• Knowledge of RAG architecture and implementation
• Understanding of AI performance optimization techniques
• Practical experience with AI testing and validation
• Awareness of cost optimization strategies for AI features
• Knowledge of ethical considerations in AI development

The main focus is on acquiring the following skills:

• Accelerating Java development workflows with AI assistance
• Implementing AI features in Java applications effectively
• Choosing appropriate AI integration approaches for specific use cases
• Building maintainable and scalable AI-enhanced applications

Training outline
Day 1: AI-Assisted Development and Integration Fundamentals

• The evolution of AI in software development and overview of AI-assisted tools (1 hour)
• GitHub Copilot deep dive: setup, configuration, and effective prompting for Java/Kotlin (2 hours)
• Practical exercises: code review, refactoring, and testing with AI assistance (1.5 hours)
• AI fundamentals for Java developers and Java AI libraries overview (1.5 hours)
• Hands-on: Building your first AI-enhanced Java application (2 hours)

Day 2: Advanced AI Integration and Production Patterns

• Cloud AI services integration with Java (AWS Bedrock, Azure OpenAI, Google AI) (1.5 hours)
• RAG (Retrieval Augmented Generation) systems with Java (2 hours)
• Local model deployment strategies and performance optimization (1.5 hours)
• Testing strategies for AI-enhanced applications and cost optimization (1.5 hours)
• Production deployment patterns, ethical considerations, and best practices (1 hour)
• Workshop: Enhancing an existing Java application with both AI assistance and AI capabilities (1.5 hours)