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Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Understanding Gemini's role and capabilities within apps
- Exploring Firebase and SDK integration strategies
- Identifying key use cases for embedded AI
Configuring the Development Environment
- Initial setup and configuration of Firebase projects
- Installation and configuration of Vertex AI SDKs
- Practical lab: Establishing the development environment
Integrating Gemini into Applications
- Making Gemini API calls from client-side applications
- Implementing text, image, and audio processing capabilities
- Practical lab: Developing a core Gemini-powered feature
Processing Multimodal Inputs
- Capturing and interpreting diverse user inputs (voice, image, text)
- Designing interactive workflows centered around Gemini
- Practical lab: Building a multimodal input feature
Deployment and Performance Monitoring
- Releasing AI-enhanced applications to production
- Tracking performance metrics and usage via Firebase
- Practical lab: Deploying and validating application functionality
Security and Compliance Protocols
- Adopting best practices for AI data handling
- Ensuring user privacy and managing consent within apps
- Practical lab: Implementing security measures for AI features
Case Studies and Industry Best Practices
- Analyzing Gemini implementations in consumer and enterprise sectors
- Extracting insights from real-world deployment experiences
- Applying best practices for scalable AI features in applications
Course Conclusion and Recommended Next Steps
Requirements
- Fundamental programming skills 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 engineering teams
14 Hours
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