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Course Outline
Introduction to Vertex AI for Mobile & Web Apps
- Overview of Gemini's capabilities within applications.
- Integration pathways involving Firebase and SDKs.
- Key use cases for embedded AI solutions.
Setting Up the Development Environment
- Creation and configuration of Firebase projects.
- Installation and setup of Vertex AI SDKs.
- Hands-on lab: Environment configuration.
Embedding Gemini into Applications
- Invoking Gemini APIs from client-side applications.
- Integrating capabilities for text, image, and audio processing.
- Hands-on lab: Developing a Gemini-powered feature.
Multimodal Input Handling
- Capturing and processing user inputs (voice, image, text).
- Designing interactive app workflows powered by Gemini.
- Hands-on lab: Implementing multimodal input features.
App Deployment and Monitoring
- Deploying AI-enabled applications to production.
- Monitoring performance metrics and usage patterns via Firebase.
- Hands-on lab: Deploying and testing applications.
Security and Compliance Considerations
- Best practices for data handling in AI features.
- Ensuring user privacy and obtaining consent within apps.
- Hands-on lab: Securing AI functionalities.
Case Studies and Best Practices
- Examples of Gemini integration in consumer and enterprise applications.
- Key insights derived from real-world implementations.
- Best practices for building scalable AI features in apps.
Summary and Next Steps
Requirements
- Fundamental programming proficiency in JavaScript, Kotlin, or Swift.
- Familiarity with mobile or web application development processes.
- Prior experience working with Firebase or cloud SDKs.
Target Audience
- Mobile application developers.
- Web application developers.
- Product management and development teams.
14 Hours
Testimonials (1)
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