Generative AI with Azure OpenAI for Java Developers

• An understanding of Java programming
• Experience with RESTful APIs and web services
• Familiarity with cloud computing concepts

Audience

• Java developers
• Software engineers
• Cloud enthusiasts

Generative AI is revolutionizing the way we build software.

This instructor-led, live training (online or onsite) is aimed at intermediate-level Java developers, software engineers, and cloud enthusiasts who wish to harness the power of Azure OpenAI to create intelligent applications.

By the end of this training, participants will be able to:

• Understand the principles of Generative AI and its applications.
• Set up and manage an Azure OpenAI service.
• Integrate OpenAI's models into Java applications.
• Deploy AI-powered features within web applications.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Generative AI and Azure OpenAI

• The landscape of AI and Generative AI
• Overview of Azure OpenAI services
• Setting up an Azure account and OpenAI service

Working with Azure OpenAI Studio and Playground

• Navigating Azure OpenAI Studio
• Experimenting with models in the Playground
• Understanding model capabilities and limitations

Integrating OpenAI with Java

• Setting up the Java development environment
• Accessing Azure OpenAI using Java
• Building and testing AI features in Java applications
• Introduction to ChatGPT and its integration with Java
• Understanding and applying Prompt Engineering techniques

Deploying AI Models as Web Applications

• Creating web applications with Java
• Embedding AI functionalities in web apps
• Best practices for deployment and scaling

Image Generation with DALL-E

• Introduction to DALL-E and image generation
• Using DALL-E studio to create images
• Generating images from Java code

Text Embeddings and Semantic Search

• Understanding text embeddings
• Implementing embedding models in Java
• Building semantic search features

Voice Processing with Whisper AI

• Basics of audio processing with AI
• Using Whisper AI for speech-to-text
• Audio translation and multilingual support

Advanced AI Model Integration

• Combining text and audio models
• Personalizing AI interactions with user data
• Implementing keyword and vector search
• Enhancing interactions with ChatGPT and Prompt Engineering

Security and Fine-Tuning

• Securing AI-powered applications
• Fine-tuning models for specific use cases
• Using content filters to maintain quality

Practical Sessions

• Hands-on labs with real-world scenarios
• Collaborative projects and peer reviews
• Capstone project: Building an AI-powered Java application

Summary and Next Steps