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Duration 14 hours
Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Exploring Gemini's capabilities within application contexts
- Understanding integration pathways through Firebase and SDKs
- Identifying key use cases for embedded AI solutions
Configuring the Development Environment
- Establishing and configuring Firebase projects
- Installing and setting up Vertex AI SDKs
- Hands-on lab: Initializing the development environment
Integrating Gemini into Applications
- Executing Gemini API calls from client-side applications
- Incorporating text, image, and audio processing capabilities
- Hands-on lab: Developing a Gemini-enabled feature
Managing Multimodal Input
- Capturing and processing diverse user inputs, including voice, images, and text
- Designing interactive application workflows driven by Gemini
- Hands-on lab: Implementing multimodal input functionalities
Application Deployment and Monitoring
- Deploying AI-enhanced applications to production environments
- Tracking performance and usage metrics via Firebase
- Hands-on lab: Deploying and testing application releases
Security and Compliance Frameworks
- Implementing best practices for data handling in AI features
- Ensuring user privacy and consent compliance within apps
- Hands-on lab: Securing AI-driven functionalities
Case Studies and Industry Best Practices
- Examining Gemini implementations in consumer and enterprise applications
- Analyzing insights gained from real-world deployment scenarios
- Adopting best practices for scalable in-app AI features
Summary and Future Directions
Requirements
- Fundamental programming proficiency in JavaScript, Kotlin, or Swift
- Proficiency in mobile or web application development
- Practical experience with Firebase or other cloud SDKs
Target Audience
- Mobile developers
- Web developers
- Product development teams
Testimonials (1)
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