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Course Outline
Introduction to Vertex AI for Mobile & Web Apps
- Overview of Gemini’s capabilities within application contexts
- Integration pathways using Firebase and SDKs
- Key use cases for embedded AI solutions
Establishing the Development Environment
- Configuring Firebase projects
- Installing and setting up Vertex AI SDKs
- Hands-on lab: Initial environment configuration
Integrating Gemini into Applications
- Invoking Gemini APIs from client-side applications
- Incorporating text, image, and audio processing capabilities
- Hands-on lab: Developing a Gemini-driven feature
Processing Multimodal Inputs
- Capturing and processing diverse user inputs (voice, images, text)
- Designing interactive workflows powered by Gemini
- Hands-on lab: Implementing a multimodal input feature
Application Deployment and Monitoring
- Releasing AI-enabled apps to production
- Tracking performance and usage metrics via Firebase
- Hands-on lab: Deployment and testing protocols
Security and Compliance Perspectives
- Best practices for data management in AI features
- Managing user privacy and consent mechanisms
- Hands-on lab: Securing AI functionalities
Case Studies and Industry Best Practices
- Real-world examples of Gemini in consumer and enterprise sectors
- Key insights from practical implementations
- Strategies for scalable AI feature development
Summary and Next Steps
Requirements
- Foundational programming proficiency in JavaScript, Kotlin, or Swift
- Working knowledge of mobile or web application development
- Experience with Firebase or other cloud SDKs
Target Audience
- Mobile developers
- Web developers
- Product teams
14 Hours
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