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Duration 14 hours
Course Outline
Introduction to Vertex AI for Mobile & Web Applications
- An overview of Gemini’s capabilities within application contexts.
- Exploring integration pathways with Firebase and SDKs.
- Identifying key use cases for embedded AI solutions.
Configuring the Development Environment
- Setting up and configuring Firebase projects.
- Installing and tuning Vertex AI SDKs.
- Practical lab: Initializing the development environment.
Integrating Gemini into Applications
- Invoking Gemini APIs from client-side applications.
- Incorporating text, image, and audio processing capabilities.
- Practical lab: Developing a Gemini-enhanced feature.
Managing Multimodal Inputs
- Capturing and processing various user inputs, including voice, images, and text.
- Designing interactive workflows that leverage Gemini.
- Practical lab: Implementing a multimodal input feature.
Application Deployment and Monitoring
- Releasing AI-enabled applications to production.
- Tracking performance and usage metrics via Firebase.
- Practical lab: Deploying and testing application instances.
Addressing Security and Compliance
- Implementing best practices for data management in AI features.
- Ensuring user privacy and obtaining consent within applications.
- Practical lab: Securing AI-driven functionality.
Case Studies and Industry Best Practices
- Examining examples of Gemini usage in consumer and enterprise applications.
- Learning from real-world implementation challenges and solutions.
- Adopting best practices for building scalable in-app AI features.
Conclusion and Future Recommendations
Requirements
- Foundational programming skills in JavaScript, Kotlin, or Swift.
- A solid understanding of mobile or web application development workflows.
- Prior experience working with Firebase or other cloud-based SDKs.
Target Audience
- Mobile application developers.
- Web developers.
- Product engineering teams.
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