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Course Outline

Introduction to Vertex AI for Mobile and Web Applications

  • Overview of Gemini capabilities within applications
  • Pathways for Firebase and SDK integration
  • Use cases for embedded AI

Setting Up the Development Environment

  • Configuration and setup of Firebase projects
  • Installation and configuration of Vertex AI SDKs
  • Practical lab: environment setup

Integrating Gemini into Applications

  • Invoking Gemini APIs from client-side applications
  • Incorporating text, image, and audio functionalities
  • Practical lab: constructing a Gemini-powered feature

Multimodal Input Processing

  • Capturing and processing user inputs (voice, image, text)
  • Developing interactive application workflows with Gemini
  • Practical lab: implementing multimodal input features

Application Deployment and Monitoring

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

Security and Compliance Considerations

  • Data handling best practices for AI features
  • Ensuring user privacy and obtaining consent within applications
  • Practical lab: securing an AI feature

Case Studies and Best Practices

  • Examples of Gemini integration in consumer and enterprise applications
  • Insights gained from real-world implementations
  • Best practices for scalable AI features in applications

Summary and Next Steps

Requirements

  • Fundamental programming knowledge in JavaScript, Kotlin, or Swift
  • Understanding of mobile or web application development
  • Experience with Firebase or cloud SDKs

Target Audience

  • Mobile developers
  • Web developers
  • Product teams
 14 Hours

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