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 Duration 14 hours

Course Outline

Introduction to Vertex AI for Mobile and Web Ecosystems

  • An overview of Gemini’s capabilities within application contexts
  • Exploring integration pathways via Firebase and SDKs
  • Identifying key use cases for embedded AI solutions

Establishing the Development Environment

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

Integrating Gemini into Application Logic

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

Managing Multimodal Input Streams

  • Capturing and processing diverse user inputs such as voice, images, and text
  • Designing interactive application workflows centered on Gemini
  • Practical lab: Implementing a multimodal input feature

Application Deployment and Oversight

  • Deploying AI-enhanced applications to production servers
  • Utilizing Firebase for performance tracking and usage analysis
  • Practical lab: Executing deployment and testing procedures

Security and Regulatory Compliance

  • Implementing data handling best practices for AI features
  • Ensuring user privacy and obtaining necessary consents within apps
  • Practical lab: Securing an AI-driven feature

Case Studies and Industry Best Practices

  • Examining Gemini implementations in consumer and enterprise applications
  • Deriving insights from real-world deployment experiences
  • Adopting best practices for scaling AI features within apps

Conclusion and Future Pathways

Requirements

  • Fundamental proficiency in JavaScript, Kotlin, or Swift.
  • General understanding of mobile or web application development principles.
  • Working experience with Firebase or other cloud-based SDKs.

Target Audience

  • Mobile application developers.
  • Web application developers.
  • Product teams and stakeholders.

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