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Duration 14 hours
Course Outline
Getting Started with Vertex AI for Mobile & Web
- Exploring the potential of Gemini within application contexts
- Understanding integration pathways via Firebase and SDKs
- Identifying key use cases for embedded AI solutions
Preparing the Development Workspace
- Initiating and configuring Firebase projects
- Installing and tuning Vertex AI SDKs
- Practical exercise: Establishing the development environment
Integrating Gemini into Application Architectures
- Executing Gemini API calls from client-side applications
- Blending text, image, and audio processing capabilities
- Practical exercise: Developing a core Gemini-driven functionality
Managing Multimodal Data Inputs
- Capturing and interpreting voice, image, and text data from users
- Designing interactive workflows enhanced by Gemini
- Practical exercise: Implementing a multimodal input feature
Releasing and Overseeing AI Applications
- Strategies for deploying AI-enabled apps to production
- Tracking performance metrics and usage via Firebase
- Practical exercise: Deployment workflows and testing protocols
Navigating Security and Regulatory Compliance
- Adopting best practices for secure AI data handling
- Ensuring user privacy and informed consent within apps
- Practical exercise: Hardening the security of an AI feature
Real-World Applications and Industry Standards
- Case studies of Gemini in consumer and enterprise settings
- Key insights drawn from live implementations
- Frameworks for building scalable AI features
Recap and Future Directions
Requirements
- Fundamental programming proficiency in JavaScript, Kotlin, or Swift
- Working knowledge of mobile or web application development lifecycles
- Prior experience with Firebase or other cloud-based SDKs
Target Audience
- Mobile application developers
- Web developers
- Product engineering teams
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