Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Vertex AI for Mobile & Web Apps
- Overview of Gemini capabilities within applications.
- Integration pathways for Firebase and SDKs.
- Use cases for embedded AI solutions.
Setting Up the Development Environment
- Creation and configuration of Firebase projects.
- Installation and configuration of Vertex AI SDKs.
- Hands-on lab: Environment setup.
Embedding Gemini into Applications
- Invoking Gemini APIs from client applications.
- Integrating text, image, and audio functionalities.
- Hands-on lab: Building a Gemini-powered feature.
Multimodal Input Handling
- Capturing and processing user inputs (voice, image, text).
- Designing interactive application workflows with Gemini.
- Hands-on lab: Implementing multimodal input features.
App Deployment and Monitoring
- Deploying AI-enabled applications to production.
- Monitoring performance and usage metrics with Firebase.
- Hands-on lab: Deploying and testing applications.
Security and Compliance Considerations
- Data handling best practices for AI features.
- Ensuring user privacy and consent within applications.
- Hands-on lab: Securing an AI feature.
Case Studies and Best Practices
- Examples of Gemini integration in consumer and enterprise applications.
- Lessons learned from real-world implementations.
- Best practices for scalable AI features in applications.
Summary and Next Steps
Requirements
- Fundamental programming knowledge in JavaScript, Kotlin, or Swift.
- Understanding of mobile or web application development.
- Prior experience using Firebase or cloud SDKs.
Audience
- Mobile developers.
- Web developers.
- Product teams.
14 Hours
Testimonials (1)
easy steps in ML