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 Duration 14 hours

Course Outline

Foundations of Vertex AI for Mobile & Web Apps

  • Overview of Gemini's capabilities within application contexts
  • Integration routes using Firebase and SDKs
  • Real-world use cases for embedded AI solutions

Configuring the Development Environment

  • Initializing and configuring Firebase projects
  • Installation and setup of Vertex AI SDKs
  • Practical session: Environment configuration

Integrating Gemini into Applications

  • Invoking Gemini APIs from client-side applications
  • Combining text, image, and audio processing capabilities
  • Practical session: Developing a Gemini-powered feature

Managing Multimodal Inputs

  • Capture and processing of user inputs, including voice, images, and text
  • Designing interactive application workflows with Gemini
  • Practical session: Implementing multimodal input features

Application Deployment and Monitoring

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

Addressing Security and Compliance

  • Best practices for handling data within AI features
  • Ensuring user privacy and obtaining consent in applications
  • Practical session: Securing AI-driven functionalities

Case Studies and Best Practices

  • Examples of Gemini in consumer and enterprise applications
  • Insights gained from real-world implementations
  • Strategies for building scalable AI features within apps

Conclusion and Future Steps

Requirements

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

Target Audience

  • Mobile Developers
  • Web Developers
  • Product Teams

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