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Duration 14 hours
Course Outline
Introduction to Vertex AI for Mobile and Web Ecosystems
- An overview of Gemini’s capabilities within application contexts
- Exploring integration pathways via Firebase and SDKs
- Identifying key use cases for embedded AI solutions
Establishing the Development Environment
- Initializing and configuring Firebase projects
- Installing and setting up Vertex AI SDKs
- Practical lab: Configuring the development environment
Integrating Gemini into Application Logic
- Executing Gemini API calls from client-side applications
- Incorporating text, image, and audio processing capabilities
- Practical lab: Developing a feature powered by Gemini
Managing Multimodal Input Streams
- Capturing and processing diverse user inputs such as voice, images, and text
- Designing interactive application workflows centered on Gemini
- Practical lab: Implementing a multimodal input feature
Application Deployment and Oversight
- Deploying AI-enhanced applications to production servers
- Utilizing Firebase for performance tracking and usage analysis
- Practical lab: Executing deployment and testing procedures
Security and Regulatory Compliance
- Implementing data handling best practices for AI features
- Ensuring user privacy and obtaining necessary consents within apps
- Practical lab: Securing an AI-driven feature
Case Studies and Industry Best Practices
- Examining Gemini implementations in consumer and enterprise applications
- Deriving insights from real-world deployment experiences
- Adopting best practices for scaling AI features within apps
Conclusion and Future Pathways
Requirements
- Fundamental proficiency in JavaScript, Kotlin, or Swift.
- General understanding of mobile or web application development principles.
- Working experience with Firebase or other cloud-based SDKs.
Target Audience
- Mobile application developers.
- Web application developers.
- Product teams and stakeholders.
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