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Duration 14 hours
Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Overview of Gemini’s capabilities within app environments
- Integration pathways using Firebase and SDKs
- Key use cases for embedded artificial intelligence
Configuring the Development Environment
- Setting up and configuring Firebase projects
- Installing and tuning Vertex AI SDKs
- Practical lab: Initializing the development environment
Integrating Gemini into Application Logic
- Invoking Gemini APIs from client-side applications
- Incorporating text, image, and audio processing capabilities
- Practical lab: Developing a feature powered by Gemini
Managing Multimodal Input Streams
- Capturing and processing diverse user inputs, including voice, image, and text
- Designing interactive workflows leveraging Gemini
- Practical lab: Implementing multimodal input features
Application Deployment and Performance Monitoring
- Releasing AI-enhanced applications to production
- Tracking performance metrics and usage via Firebase
- Practical lab: Deployment and testing workflows
Addressing Security and Compliance Requirements
- Best practices for data management in AI-centric features
- Ensuring user privacy and managing consent mechanisms within apps
- Practical lab: Securing AI-driven functionality
Case Studies and Industry Best Practices
- Analyzing Gemini implementations in consumer and enterprise contexts
- Insights derived from real-world deployment experiences
- Strategies for building scalable AI features within applications
Wrap-up and Future Directions
Requirements
- Fundamental programming skills in JavaScript, Kotlin, or Swift
- Working knowledge of mobile or web application development workflows
- Prior experience with Firebase or other cloud-based SDKs
Target Audience
- Mobile application developers
- Web application developers
- Product management and engineering teams
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