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 Duration 14 hours

Course Outline

Introduction to Vertex AI for Mobile & Web Applications

  • Exploring Gemini capabilities within app environments
  • Integration pathways for Firebase and SDKs
  • Key use cases for embedded AI solutions

Configuring the Development Environment

  • Establishing and configuring Firebase projects
  • Installing and setting up Vertex AI SDKs
  • Practical lab: Environment configuration

Integrating Gemini into Applications

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

Managing Multimodal Inputs

  • Capturing and processing user inputs (voice, image, text)
  • Designing interactive workflows using Gemini
  • Practical lab: Implementing multimodal input features

Application Deployment and Monitoring

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

Security and Compliance Factors

  • Best practices for data management in AI features
  • Ensuring user privacy and consent protocols in apps
  • Practical lab: Securing AI-driven functionalities

Case Studies and Best Practices

  • Real-world examples of Gemini in consumer and enterprise apps
  • Insights gained from practical implementations
  • Strategies for building scalable AI features

Recap and Future Directions

Requirements

  • Fundamental programming skills in JavaScript, Kotlin, or Swift
  • Understanding of mobile or web application development principles
  • Prior experience working with Firebase or cloud-based SDKs

Target Audience

  • Mobile application developers
  • Web developers
  • Product development teams

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