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 Duration 14 hours

Course Outline

Introduction to Vertex AI for Mobile & Web Applications

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

Establishing the Development Environment

  • Firebase project initialization and configuration
  • Installation and setup of Vertex AI SDKs
  • Hands-on lab: environment preparation

Integrating Gemini into Applications

  • Invoking Gemini APIs from client applications
  • Incorporating text, image, and audio processing capabilities
  • Hands-on lab: developing a Gemini-powered feature

Managing Multimodal Inputs

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

Application Deployment and Monitoring

  • Releasing AI-enabled apps to production
  • Tracking performance and usage metrics via Firebase
  • Hands-on lab: deploying and testing applications

Addressing Security and Compliance

  • Best practices for data management in AI features
  • User privacy considerations and consent mechanisms in apps
  • Hands-on lab: securing AI functionalities

Case Studies and Industry Best Practices

  • Illustrations of Gemini in consumer and enterprise applications
  • Insights from real-world implementation experiences
  • Strategies for scalable AI features in applications

Wrap-Up and Future Steps

Requirements

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

Target Audience

  • Mobile developers
  • Web developers
  • Product teams

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