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Duration 14 hours
Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Overview of Gemini's capabilities in application contexts
- Integration pathways via Firebase and SDKs
- Key use cases for embedded AI solutions
Establishing the Development Environment
- Setting up and configuring Firebase projects
- Installing and configuring Vertex AI SDKs
- Hands-on lab: Initial environment setup
Integrating Gemini into Applications
- Invoking Gemini APIs from client-side apps
- Incorporating text, image, and audio processing capabilities
- Hands-on lab: Developing a Gemini-driven feature
Managing Multimodal Inputs
- Capturing and processing diverse user inputs (voice, image, text)
- Designing interactive app workflows with Gemini
- Hands-on lab: Implementing multimodal input features
Application Deployment and Monitoring
- Releasing AI-powered applications to production
- Tracking performance and usage metrics using Firebase
- Hands-on lab: Deploying and testing applications
Security and Compliance Considerations
- Best practices for data handling in AI features
- Ensuring user privacy and consent within apps
- Hands-on lab: Securing an AI feature implementation
Case Studies and Industry Best Practices
- Real-world examples of Gemini in consumer and enterprise apps
- Insights gained from actual implementations
- Strategies for building scalable AI features in applications
Conclusion and Future Steps
Requirements
- Fundamental programming proficiency in JavaScript, Kotlin, or Swift
- Understanding of mobile or web application development processes
- Practical experience with Firebase or cloud-based SDKs
Target Audience
- Mobile developers
- Web developers
- Product teams
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