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Duration 14 hours
Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Overview of Gemini's capabilities within app ecosystems
- Integration pathways via Firebase and SDKs
- Practical use cases for embedded AI
Establishing the Development Environment
- Configuring Firebase projects and settings
- Setting up and installing Vertex AI SDKs
- Practical session: Environment configuration
Integrating Gemini into Applications
- Interacting with Gemini APIs from client-side code
- Incorporating text, image, and audio processing capabilities
- Practical session: Developing a Gemini-enabled feature
Managing Multimodal Inputs
- Capturing and interpreting user inputs such as voice, images, and text
- Designing interactive workflows powered by Gemini
- Practical session: Implementing multimodal input features
Application Deployment and Monitoring
- Releasing AI-integrated applications to production
- Tracking performance and usage metrics via Firebase
- Practical session: Deployment and testing procedures
Security and Compliance Factors
- Best practices for data management in AI contexts
- Ensuring user privacy and obtaining proper consent within apps
- Practical session: Securing AI-based functionalities
Case Studies and Industry Standards
- Illustrations of Gemini in consumer and enterprise products
- Insights gained from real-world deployments
- Strategies for building scalable AI features within applications
Conclusion and Future Directions
Requirements
- Fundamental programming proficiency in JavaScript, Kotlin, or Swift
- Working knowledge of mobile or web application development
- Practical experience with Firebase or other cloud-based SDKs
Target Audience
- Mobile developers
- Web developers
- Product teams
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