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 Duration 14 hours

Course Outline

Introduction to Vertex AI for Mobile & Web Applications

  • Exploring Gemini’s capabilities within application contexts
  • Reviewing integration paths via Firebase and SDKs
  • Identifying key use cases for embedded AI solutions

Configuring the Development Environment

  • Establishing and setting up Firebase projects
  • Installing and configuring the necessary Vertex AI SDKs
  • Practical lab: Initializing the development environment

Integrating Gemini into Applications

  • Executing Gemini API calls from client-side applications
  • Blending text, image, and audio processing capabilities
  • Practical lab: Developing a feature powered by Gemini

Managing Multimodal Inputs

  • Capturing and interpreting user data such as voice, images, and text
  • Designing interactive workflows utilizing Gemini
  • Practical lab: Implementing multimodal input features

Application Deployment and Monitoring

  • Releasing AI-driven applications to production
  • Tracking performance and usage metrics via Firebase
  • Practical lab: Deploying and validating application features

Security and Compliance Frameworks

  • Adhering to best practices for AI data management
  • Ensuring user privacy and consent mechanisms within apps
  • Practical lab: Enhancing the security of AI features

Case Studies and Industry Standards

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

Conclusion and Future Directions

Requirements

  • Fundamental proficiency in JavaScript, Kotlin, or Swift
  • Working knowledge of mobile or web application development
  • Prior experience with Firebase or other cloud-based SDKs

Target Audience

  • Mobile Developers
  • Web Developers
  • Product Teams

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