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 Duration 14 hours

Course Outline

Introduction to Vertex AI for Mobile & Web Applications

  • Overview of Gemini's potential within applications
  • Integration pathways for Firebase and SDKs
  • Practical use cases for embedded AI

Configuring the Development Environment

  • Setting up and configuring Firebase projects
  • Installation and configuration of Vertex AI SDKs
  • Practical session: establishing the development environment

Integrating Gemini into Applications

  • Invoking Gemini APIs from client applications
  • Incorporating text, image, and audio capabilities
  • Practical session: developing a feature powered by Gemini

Managing Multimodal Inputs

  • Capturing and processing diverse user inputs (voice, images, text)
  • Designing interactive workflows with Gemini
  • Practical session: implementing a multimodal input feature

Application Deployment and Oversight

  • Releasing AI-enabled applications to production
  • Tracking performance and usage metrics via Firebase
  • Practical session: deploying and validating applications

Addressing Security and Compliance

  • Best practices for data management in AI features
  • Ensuring user privacy and obtaining consent in applications
  • Practical session: implementing security measures for an AI feature

Case Studies and Industry Best Practices

  • Examples of Gemini in consumer and enterprise contexts
  • Key takeaways from real-world deployments
  • Strategies for scalable AI features in applications

Recap and Future Directions

Requirements

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

Target Audience

  • Mobile application developers
  • Web developers
  • Product management teams

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