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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 strategies for Firebase and SDKs
- Key use cases for embedded AI solutions
Establishing the Development Environment
- Configuring Firebase projects and settings
- Installation and configuration of Vertex AI SDKs
- Practical lab: Setting up the development environment
Integrating Gemini into Applications
- Invoking Gemini APIs from client-side applications
- Incorporating text, image, and audio processing capabilities
- Practical lab: Developing a Gemini-driven feature
Managing Multimodal Input
- Capturing and processing diverse user inputs (voice, image, text)
- Designing interactive app workflows powered by Gemini
- Practical lab: Implementing multimodal input features
Application Deployment and Monitoring
- Releasing AI-enabled applications to production
- Tracking performance and usage metrics via Firebase
- Practical lab: Deploying and testing applications
Security and Compliance Frameworks
- Best practices for data handling in AI features
- Ensuring user privacy and consent within applications
- Practical lab: Securing AI functionality
Case Studies and Best Practices
- Real-world examples of Gemini in consumer and enterprise apps
- Key takeaways from practical implementations
- Strategies for scaling AI features within apps
Conclusion and Future Steps
Requirements
- Fundamental programming proficiency in JavaScript, Kotlin, or Swift.
- Working knowledge of mobile or web application development.
- Hands-on experience with Firebase or other cloud-based SDKs.
Target Audience
- Mobile developers
- Web developers
- Product teams
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