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Duration 14 hours
Course Outline
Foundations of Vertex AI for Mobile & Web Applications
- Exploring Gemini’s capabilities within application contexts
- Understanding integration pathways for Firebase and SDKs
- Identifying key use cases for embedded AI
Configuring the Development Environment
- Initiating and configuring Firebase projects
- Installing and setting up Vertex AI SDKs
- Practical lab: establishing the development environment
Integrating Gemini into Application Logic
- Invoking Gemini APIs from client-side applications
- Incorporating text, image, and audio processing capabilities
- Practical lab: developing a core Gemini-powered functionality
Managing Multimodal Input Streams
- Capturing and processing diverse user inputs (voice, image, text)
- Designing interactive workflows driven by Gemini
- Practical lab: implementing a multimodal input feature
Deployment and Operational Monitoring
- Releasing AI-enabled applications to production
- Tracking performance and usage metrics via Firebase
- Practical lab: executing deployment and testing cycles
Navigating Security and Compliance Frameworks
- Applying best practices for data management in AI features
- Ensuring user privacy and consent protocols in applications
- Practical lab: implementing security measures for an AI feature
Real-World Case Studies and Industry Standards
- Analyzing Gemini implementations in consumer and enterprise sectors
- Deriving insights from live production deployments
- Adopting best practices for scalable in-app AI features
Concluding Summary and Future Pathways
Requirements
- Fundamental programming proficiency in JavaScript, Kotlin, or Swift
- General familiarity with mobile or web application development workflows
- Practical experience with Firebase or other cloud-based SDKs
Target Audience
- Mobile application developers
- Web developers
- Product development teams
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