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 Duration 14 hours

Course Outline

Introduction to Vertex AI for Mobile & Web Applications

  • An overview of Gemini’s capabilities within applications
  • Integration pathways using Firebase and SDKs
  • Real-world use cases for embedded AI

Establishing the Development Environment

  • Setting up and configuring Firebase projects
  • Installing and configuring Vertex AI SDKs
  • Practical lab: Setting up the environment

Integrating Gemini into Applications

  • Making Gemini API calls from client-side applications
  • Incorporating text, image, and audio capabilities
  • Practical lab: Developing a Gemini-powered feature

Handling Multimodal Inputs

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

Application Deployment and Monitoring

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

Security and Compliance Considerations

  • Best practices for data handling in AI features
  • Ensuring user privacy and managing consent in apps
  • Practical lab: Securing an AI feature

Case Studies and Best Practices

  • Examples of Gemini deployment in consumer and enterprise apps
  • Key takeaways from real-world implementations
  • Strategies for building scalable AI features in applications

Summary and Future Steps

Requirements

  • Foundational programming knowledge in JavaScript, Kotlin, or Swift
  • A solid understanding of mobile or web application development
  • Prior experience working with Firebase or cloud-based SDKs

Target Audience

  • Mobile developers
  • Web developers
  • Product teams

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Provisional Upcoming Courses (Require 5+ participants)

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