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 Duration 14 hours

Course Outline

Introduction to Vertex AI for Mobile & Web Apps

  • Overview of Gemini's capabilities within applications
  • Integration pathways for Firebase and SDKs
  • Use cases for embedded AI solutions

Configuring the Development Environment

  • Setting up and configuring Firebase projects
  • Installing and configuring 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 functionalities
  • Practical lab: constructing a feature powered by Gemini

Managing Multimodal Inputs

  • Capturing and processing user inputs, including voice, images, and text
  • Designing interactive application workflows with Gemini
  • Practical lab: implementing a multimodal input feature

Application Deployment and Monitoring

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

Security and Compliance Considerations

  • Best practices for data management in AI features
  • Ensuring user privacy and consent within applications
  • Practical lab: implementing security measures for an AI feature

Case Studies and Best Practices

  • Illustrations of Gemini usage in consumer and enterprise applications
  • Insights derived from real-world implementations
  • Best practices for achieving scalable AI features in applications

Recap and Recommended Next Steps

Requirements

  • Fundamental programming knowledge in JavaScript, Kotlin, or Swift
  • Understanding of mobile or web application development principles
  • Practical experience 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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