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 Duration 14 hours

Course Outline

Introduction to Vertex AI for Mobile & Web Applications

  • Examining Gemini's core capabilities within applications
  • Exploring integration pathways via Firebase and SDKs
  • Identifying key use cases for embedded AI solutions

Configuring the Development Environment

  • Establishing and configuring Firebase projects
  • Setting up Vertex AI SDKs for development
  • Practical lab: Initializing the development environment

Integrating Gemini into Applications

  • Invoking Gemini APIs from client-side applications
  • Implementing text, image, and audio interaction capabilities
  • Practical lab: Developing a feature powered by Gemini

Managing Multimodal Inputs

  • Capturing and processing diverse user inputs (voice, images, text)
  • Designing interactive workflows with Gemini
  • Practical lab: Implementing a multimodal input handler

Application Deployment and Monitoring

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

Security and Compliance Frameworks

  • Adopting best practices for data management in AI features
  • Ensuring user privacy and consent compliance in applications
  • Practical lab: Securing AI feature implementations

Case Studies and Industry Standards

  • Analyzing Gemini implementations in consumer and enterprise contexts
  • Extracting insights from real-world deployment experiences
  • Applying best practices for scalable in-app AI features

Conclusion and Future Directions

Requirements

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

Target Audience

  • Mobile developers
  • Web developers
  • Product development teams

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