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Course Outline

Introduction to Vertex AI for Mobile & Web Applications

  • Exploring Gemini capabilities within applications
  • Firebase and SDK integration strategies
  • Key use cases for embedded AI solutions

Configuring the Development Environment

  • Establishing and configuring Firebase projects
  • Installing and setting up Vertex AI SDKs
  • Practical exercise: Setting up the development environment

Integrating Gemini into Applications

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

Managing Multimodal Input

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

Application Deployment and Monitoring

  • Releasing AI-driven applications to production
  • Tracking performance and usage metrics via Firebase
  • Practical exercise: Deploying and testing applications

Addressing Security and Compliance

  • Best practices for data management in AI features
  • Ensuring user privacy and consent mechanisms in apps
  • Practical exercise: Securing an AI feature

Case Studies and Best Practices

  • Illustrations of Gemini in consumer and enterprise contexts
  • Insights from real-world implementation challenges
  • Strategies for scaling AI features effectively

Conclusion and Future Steps

Requirements

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

Target Audience

  • Mobile developers
  • Web developers
  • Product teams
 14 Hours

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