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Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Overview of Gemini’s capabilities within applications
- Integration pathways via Firebase and SDKs
- Practical use cases for embedded AI
Establishing the Development Environment
- Initializing and configuring Firebase projects
- Installing and setting up Vertex AI SDKs
- Hands-on lab: Environment configuration
Integrating Gemini into Applications
- Invoking Gemini APIs from client-side applications
- Incorporating text, image, and audio processing features
- Hands-on lab: Developing a Gemini-powered component
Managing Multimodal Inputs
- Capturing and processing diverse user inputs (voice, image, text)
- Designing interactive workflows powered by Gemini
- Hands-on lab: Implementing multimodal input features
Application Deployment and Monitoring
- Releasing AI-enabled apps to production environments
- Tracking performance and usage metrics via Firebase
- Hands-on lab: Deploying and testing applications
Security and Compliance Factors
- Best practices for managing data in AI features
- User privacy protocols and consent mechanisms in apps
- Hands-on lab: Securing AI functionality
Case Studies and Industry Best Practices
- Examples of Gemini usage in consumer and enterprise contexts
- Insights gained from real-world deployments
- Strategies for scaling AI features within applications
Course Wrap-up and Future Directions
Requirements
- Fundamental programming proficiency in JavaScript, Kotlin, or Swift
- Working knowledge of mobile or web application development
- Prior experience utilizing Firebase or cloud-based SDKs
Target Audience
- Mobile developers
- Web developers
- Product teams
14 Hours
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