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Duration 14 hours
Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- An overview of Gemini’s capabilities within applications
- Integration pathways using Firebase and SDKs
- Real-world use cases for embedded AI
Establishing the Development Environment
- Setting up and configuring Firebase projects
- Installing and configuring Vertex AI SDKs
- Practical lab: Setting up the environment
Integrating Gemini into Applications
- Making Gemini API calls from client-side applications
- Incorporating text, image, and audio capabilities
- Practical lab: Developing a Gemini-powered feature
Handling Multimodal Inputs
- Capturing and processing diverse user inputs (voice, image, text)
- Designing interactive app workflows with Gemini
- Practical lab: Implementing a multimodal input feature
Application Deployment and Monitoring
- Releasing AI-powered apps to production
- Tracking performance and usage metrics via Firebase
- Practical lab: Deploying and testing applications
Security and Compliance Considerations
- Best practices for data handling in AI features
- Ensuring user privacy and managing consent in apps
- Practical lab: Securing an AI feature
Case Studies and Best Practices
- Examples of Gemini deployment in consumer and enterprise apps
- Key takeaways from real-world implementations
- Strategies for building scalable AI features in applications
Summary and Future Steps
Requirements
- Foundational programming knowledge in JavaScript, Kotlin, or Swift
- A solid understanding of mobile or web application development
- Prior experience working with Firebase or cloud-based SDKs
Target Audience
- Mobile developers
- Web developers
- Product teams
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