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Duration 14 hours
Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Exploring Gemini's capabilities within applications
- Integration strategies using Firebase and SDKs
- Key use cases for embedded AI
Establishing the Development Environment
- Initial Firebase project setup and configuration
- Installation and configuration of Vertex AI SDKs
- Practical session: Setting up the development environment
Integrating Gemini into Applications
- Invoking Gemini APIs from client-side applications
- Blending text, image, and audio capabilities
- Practical session: Developing a Gemini-driven feature
Managing Multimodal Inputs
- Capturing and processing diverse user inputs (voice, image, text)
- Designing interactive app workflows with Gemini
- Practical session: Implementing multimodal input features
Application Deployment and Monitoring
- Releasing AI-enhanced apps to production
- Tracking performance and usage metrics via Firebase
- Practical session: Deploying and testing applications
Security and Compliance Essentials
- Best practices for data management in AI features
- Ensuring user privacy and consent within applications
- Practical session: Securing AI functionalities
Case Studies and Industry Best Practices
- Real-world examples of Gemini in consumer and enterprise apps
- Insights from successful live implementations
- Strategies for building scalable AI features in apps
Wrap-up and Future Directions
Requirements
- Fundamental programming proficiency in JavaScript, Kotlin, or Swift
- General knowledge of mobile or web application development
- Practical experience with Firebase or cloud-based SDKs
Target Audience
- Mobile application developers
- Web developers
- Product management teams
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