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Duration 14 hours
Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Overview of Gemini’s capabilities within applications
- Integration pathways for Firebase and SDKs
- Key use cases for embedded AI
Configuring the Development Environment
- Setting up and configuring Firebase projects
- Installing and configuring Vertex AI SDKs
- Practical lab: Environment setup
Integrating Gemini into Applications
- Invoking Gemini APIs from client-side applications
- Integrating text, image, and audio functionalities
- Practical lab: Developing a Gemini-driven feature
Managing Multimodal Inputs
- Capturing and processing user inputs (voice, image, text)
- Designing interactive app workflows with Gemini
- Practical lab: Implementing multimodal input features
Application Deployment and Monitoring
- Deploying AI-enabled apps to production environments
- Monitoring performance and usage via Firebase
- Practical lab: Deploying and testing applications
Security and Compliance Essentials
- Best practices for data handling in AI features
- User privacy and consent mechanisms in apps
- Practical lab: Securing an AI feature
Case Studies and Industry Best Practices
- Examples of Gemini usage in consumer and enterprise applications
- Insights gained from real-world implementations
- Best practices for scaling AI features within apps
Summary and Future Steps
Requirements
- Fundamental programming proficiency in JavaScript, Kotlin, or Swift
- Working knowledge of mobile or web application development
- Practical experience with Firebase or other cloud SDKs
Target Audience
- Mobile developers
- Web developers
- Product teams
Testimonials (1)
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