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Duration 14 hours
Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- Exploring Gemini’s capabilities within application contexts
- Reviewing integration paths via Firebase and SDKs
- Identifying key use cases for embedded AI solutions
Configuring the Development Environment
- Establishing and setting up Firebase projects
- Installing and configuring the necessary Vertex AI SDKs
- Practical lab: Initializing the development environment
Integrating Gemini into Applications
- Executing Gemini API calls from client-side applications
- Blending text, image, and audio processing capabilities
- Practical lab: Developing a feature powered by Gemini
Managing Multimodal Inputs
- Capturing and interpreting user data such as voice, images, and text
- Designing interactive workflows utilizing Gemini
- Practical lab: Implementing multimodal input features
Application Deployment and Monitoring
- Releasing AI-driven applications to production
- Tracking performance and usage metrics via Firebase
- Practical lab: Deploying and validating application features
Security and Compliance Frameworks
- Adhering to best practices for AI data management
- Ensuring user privacy and consent mechanisms within apps
- Practical lab: Enhancing the security of AI features
Case Studies and Industry Standards
- Analyzing Gemini implementations in consumer and enterprise sectors
- Extracting insights from real-world deployment experiences
- Applying best practices for scalable in-app AI features
Conclusion and Future Directions
Requirements
- Fundamental proficiency in JavaScript, Kotlin, or Swift
- Working knowledge of mobile or web application development
- Prior experience with Firebase or other cloud-based SDKs
Target Audience
- Mobile Developers
- Web Developers
- Product Teams
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