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Duration 14 hours
Course Outline
Foundations of Vertex AI for Mobile & Web Applications
- An overview of Gemini’s capabilities within application contexts.
- Exploring integration pathways between Firebase and SDKs.
- Identifying key use cases for embedded AI solutions.
Establishing the Development Environment
- Setting up and configuring Firebase projects.
- Installing and configuring Vertex AI SDKs.
- Practical exercise: Setting up the development environment.
Integrating Gemini into Application Logic
- Invoking Gemini APIs from client-side applications.
- Incorporating text, image, and audio processing capabilities.
- Practical exercise: Developing a Gemini-driven feature.
Managing Multimodal Inputs
- Capturing and processing diverse user inputs, including voice, images, and text.
- Designing interactive workflows powered by Gemini.
- Practical exercise: Implementing multimodal input features.
Deployment and Performance Monitoring
- Releasing AI-enhanced applications to production environments.
- Tracking performance metrics and usage via Firebase.
- Practical exercise: Deploying and testing application stability.
Addressing Security and Compliance
- Implementing best practices for secure data handling in AI features.
- Ensuring user privacy and obtaining consent in app interactions.
- Practical exercise: Securing AI functionalities.
Real-World Case Studies and Industry Practices
- Examining Gemini implementations in both consumer and enterprise sectors.
- Analyzing insights from real-world deployment scenarios.
- Adopting best practices for scalable AI features in applications.
Conclusion and Forward Planning
Requirements
- Fundamental proficiency in JavaScript, Kotlin, or Swift.
- Working knowledge of mobile or web application development principles.
- Practical experience utilizing Firebase or other cloud-based SDKs.
Target Audience
- Mobile application developers.
- Web developers.
- Product development teams.
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