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 Duration 14 hours

Course Outline

Introduction to Vertex AI for Mobile & Web Applications

  • Overview of Gemini's capabilities in application contexts
  • Integration pathways via Firebase and SDKs
  • Key use cases for embedded AI solutions

Establishing the Development Environment

  • Setting up and configuring Firebase projects
  • Installing and configuring Vertex AI SDKs
  • Hands-on lab: Initial environment setup

Integrating Gemini into Applications

  • Invoking Gemini APIs from client-side apps
  • Incorporating text, image, and audio processing capabilities
  • Hands-on lab: Developing a Gemini-driven feature

Managing Multimodal Inputs

  • Capturing and processing diverse user inputs (voice, image, text)
  • Designing interactive app workflows with Gemini
  • Hands-on lab: Implementing multimodal input features

Application Deployment and Monitoring

  • Releasing AI-powered applications to production
  • Tracking performance and usage metrics using Firebase
  • Hands-on lab: Deploying and testing applications

Security and Compliance Considerations

  • Best practices for data handling in AI features
  • Ensuring user privacy and consent within apps
  • Hands-on lab: Securing an AI feature implementation

Case Studies and Industry Best Practices

  • Real-world examples of Gemini in consumer and enterprise apps
  • Insights gained from actual implementations
  • Strategies for building scalable AI features in applications

Conclusion and Future Steps

Requirements

  • Fundamental programming proficiency in JavaScript, Kotlin, or Swift
  • Understanding of mobile or web application development processes
  • Practical experience with Firebase or cloud-based SDKs

Target Audience

  • Mobile developers
  • Web developers
  • Product teams

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