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 Duration 14 hours

Course Outline

Introduction to Vertex AI for Mobile & Web Applications

  • Overview of Gemini's capabilities within app environments
  • Integration strategies for Firebase and SDKs
  • Key use cases for embedded AI solutions

Establishing the Development Environment

  • Configuring Firebase projects and settings
  • Installation and configuration of Vertex AI SDKs
  • Practical lab: Setting up 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-driven feature

Managing Multimodal Input

  • Capturing and processing diverse user inputs (voice, image, text)
  • Designing interactive app workflows powered by Gemini
  • Practical lab: Implementing multimodal input features

Application Deployment and Monitoring

  • Releasing AI-enabled applications to production
  • Tracking performance and usage metrics via Firebase
  • Practical lab: Deploying and testing applications

Security and Compliance Frameworks

  • Best practices for data handling in AI features
  • Ensuring user privacy and consent within applications
  • Practical lab: Securing AI functionality

Case Studies and Best Practices

  • Real-world examples of Gemini in consumer and enterprise apps
  • Key takeaways from practical implementations
  • Strategies for scaling AI features within apps

Conclusion and Future Steps

Requirements

  • Fundamental programming proficiency in JavaScript, Kotlin, or Swift.
  • Working knowledge of mobile or web application development.
  • Hands-on experience with Firebase or other cloud-based SDKs.

Target Audience

  • Mobile developers
  • Web developers
  • Product teams

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