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Course Outline

Introduction to Vertex AI for Mobile & Web Applications

  • Overview of Gemini capabilities within applications
  • Integration pathways using Firebase and SDKs
  • Practical use cases for embedded AI

Setting Up the Development Environment

  • Configuring and setting up Firebase projects
  • Installing and configuring Vertex AI SDKs
  • Hands-on lab: Environment setup

Integrating Gemini into Applications

  • Invoking Gemini APIs from client-side applications
  • Integrating text, image, and audio functionalities
  • Hands-on lab: Developing a Gemini-powered feature

Handling Multi-modal Inputs

  • Capturing and processing user inputs (voice, images, text)
  • Designing interactive app workflows with Gemini
  • Hands-on lab: Implementing a multi-modal input feature

Application Deployment and Monitoring

  • Deploying AI-enhanced applications to production
  • Tracking performance and usage metrics via Firebase
  • Hands-on lab: Deploying and testing applications

Security and Compliance Considerations

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

Case Studies and Best Practices

  • Real-world examples of Gemini in consumer and enterprise apps
  • Key takeaways from industry implementations
  • Strategies for building 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
  • Previous experience with Firebase or cloud SDKs

Target Audience

  • Mobile developers
  • Web developers
  • Product management teams
 14 Hours

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