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Duration 14 hours
Course Outline
Foundations of Vertex AI for Mobile & Web Apps
- Overview of Gemini's capabilities within application contexts
- Integration routes using Firebase and SDKs
- Real-world use cases for embedded AI solutions
Configuring the Development Environment
- Initializing and configuring Firebase projects
- Installation and setup of Vertex AI SDKs
- Practical session: Environment configuration
Integrating Gemini into Applications
- Invoking Gemini APIs from client-side applications
- Combining text, image, and audio processing capabilities
- Practical session: Developing a Gemini-powered feature
Managing Multimodal Inputs
- Capture and processing of user inputs, including voice, images, and text
- Designing interactive application workflows with Gemini
- Practical session: Implementing multimodal input features
Application Deployment and Monitoring
- Releasing AI-enhanced applications to production
- Tracking performance and usage metrics via Firebase
- Practical session: Deployment and testing procedures
Addressing Security and Compliance
- Best practices for handling data within AI features
- Ensuring user privacy and obtaining consent in applications
- Practical session: Securing AI-driven functionalities
Case Studies and Best Practices
- Examples of Gemini in consumer and enterprise applications
- Insights gained from real-world implementations
- Strategies for building scalable AI features within apps
Conclusion and Future Steps
Requirements
- Fundamental programming proficiency in JavaScript, Kotlin, or Swift
- Understanding of mobile or web application development principles
- Prior experience with Firebase or cloud-based SDKs
Target Audience
- Mobile Developers
- Web Developers
- Product Teams
Testimonials (1)
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