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 Duration 14 hours

Course Outline

Foundations of Vertex AI for Mobile & Web Applications

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

Establishing the Development Environment

  • Setting up and configuring Firebase projects.
  • Installing and configuring Vertex AI SDKs.
  • Practical exercise: Setting up the development environment.

Integrating Gemini into Application Logic

  • Invoking Gemini APIs from client-side applications.
  • Incorporating text, image, and audio processing capabilities.
  • Practical exercise: Developing a Gemini-driven feature.

Managing Multimodal Inputs

  • Capturing and processing diverse user inputs, including voice, images, and text.
  • Designing interactive workflows powered by Gemini.
  • Practical exercise: Implementing multimodal input features.

Deployment and Performance Monitoring

  • Releasing AI-enhanced applications to production environments.
  • Tracking performance metrics and usage via Firebase.
  • Practical exercise: Deploying and testing application stability.

Addressing Security and Compliance

  • Implementing best practices for secure data handling in AI features.
  • Ensuring user privacy and obtaining consent in app interactions.
  • Practical exercise: Securing AI functionalities.

Real-World Case Studies and Industry Practices

  • Examining Gemini implementations in both consumer and enterprise sectors.
  • Analyzing insights from real-world deployment scenarios.
  • Adopting best practices for scalable AI features in applications.

Conclusion and Forward Planning

Requirements

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

Target Audience

  • Mobile application developers.
  • Web developers.
  • Product development teams.

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