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Duration 14 hours
Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Examining Gemini's capabilities within application contexts
- Exploring integration routes through Firebase and SDKs
- Identifying key use cases for embedded AI
Establishing the Development Environment
- Configuring Firebase projects and settings
- Installing and adjusting Vertex AI SDKs
- Practical lab: setting up the development environment
Integrating Gemini into Applications
- Invoking Gemini APIs from client-side applications
- Combining text, image, and audio functionalities
- Practical lab: developing a Gemini-enabled feature
Managing Multimodal Inputs
- Capturing and processing user inputs such as voice, images, and text
- Designing interactive app workflows powered by Gemini
- Practical lab: implementing multimodal input features
App Deployment and Monitoring
- Releasing AI-enhanced apps to production
- Tracking performance and usage metrics via Firebase
- Practical lab: deploying and testing applications
Security and Compliance Frameworks
- Adopting best practices for data management in AI features
- Safeguarding user privacy and managing consent within apps
- Practical lab: implementing security measures for AI features
Case Studies and Industry Best Practices
- Reviewing Gemini implementations in consumer and enterprise apps
- Deriving insights from real-world deployment experiences
- Applying best practices for scalable in-app AI features
Conclusion 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 application developers
- Web developers
- Product teams
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