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 Duration 14 hours

Course Outline

Getting Started with Vertex AI for Mobile & Web

  • Exploring the potential of Gemini within application contexts
  • Understanding integration pathways via Firebase and SDKs
  • Identifying key use cases for embedded AI solutions

Preparing the Development Workspace

  • Initiating and configuring Firebase projects
  • Installing and tuning Vertex AI SDKs
  • Practical exercise: Establishing the development environment

Integrating Gemini into Application Architectures

  • Executing Gemini API calls from client-side applications
  • Blending text, image, and audio processing capabilities
  • Practical exercise: Developing a core Gemini-driven functionality

Managing Multimodal Data Inputs

  • Capturing and interpreting voice, image, and text data from users
  • Designing interactive workflows enhanced by Gemini
  • Practical exercise: Implementing a multimodal input feature

Releasing and Overseeing AI Applications

  • Strategies for deploying AI-enabled apps to production
  • Tracking performance metrics and usage via Firebase
  • Practical exercise: Deployment workflows and testing protocols

Navigating Security and Regulatory Compliance

  • Adopting best practices for secure AI data handling
  • Ensuring user privacy and informed consent within apps
  • Practical exercise: Hardening the security of an AI feature

Real-World Applications and Industry Standards

  • Case studies of Gemini in consumer and enterprise settings
  • Key insights drawn from live implementations
  • Frameworks for building scalable AI features

Recap and Future Directions

Requirements

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

Target Audience

  • Mobile application developers
  • Web developers
  • Product engineering teams

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