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 Duration 14 hours

Course Outline

Introduction to Vertex AI for Mobile & Web Applications

  • An overview of Gemini's capabilities within application contexts
  • Exploring integration pathways for Firebase and SDKs
  • Identifying key use cases for embedded AI solutions

Configuring the Development Environment

  • Establishing and configuring Firebase projects
  • Installing and setting up Vertex AI SDKs
  • Hands-on lab: Building the development environment

Integrating Gemini into Applications

  • Invoking Gemini APIs from client-side applications
  • Seamlessly integrating text, image, and audio processing capabilities
  • Hands-on lab: Developing a feature powered by Gemini

Managing Multimodal Inputs

  • Capturing and processing diverse user inputs, including voice, images, and text
  • Designing interactive app workflows leveraging Gemini
  • Hands-on lab: Implementing multimodal input features

App Deployment and Monitoring

  • Releasing AI-powered applications to production
  • Tracking performance and usage metrics using Firebase
  • Hands-on lab: Deploying and testing application builds

Addressing Security and Compliance

  • Applying best practices for data management in AI features
  • Ensuring user privacy and consent compliance within apps
  • Hands-on lab: Enhancing the security posture of an AI feature

Case Studies and Best Practices

  • Examining examples of Gemini in consumer and enterprise applications
  • Deriving insights from real-world implementation experiences
  • Adopting best practices for building scalable AI features

Conclusion and Future Steps

Requirements

  • Fundamental programming proficiency in JavaScript, Kotlin, or Swift
  • General familiarity with mobile or web application development principles
  • Practical experience utilizing Firebase or other cloud-based SDKs

Target Audience

  • Mobile developers
  • Web developers
  • Product teams

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