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 via Firebase and SDKs.
- Real-world use cases for embedded AI solutions.
Setting Up the Development Environment
- Firebase project setup and configuration.
- Installation and configuration of Vertex AI SDKs.
- Hands-on lab: Environment setup.
Embedding Gemini into Applications
- Invoking Gemini APIs from client-side applications.
- Integrating text, image, and audio functionalities.
- Hands-on lab: Building a Gemini-powered feature.
Handling Multimodal Inputs
- Capturing and processing user inputs (voice, images, and text).
- Designing interactive app workflows powered by Gemini.
- Hands-on lab: Implementing a multimodal input feature.
App Deployment and Monitoring
- Deploying AI-enhanced applications to production environments.
- Monitoring performance metrics and usage patterns via Firebase.
- Hands-on lab: Deploying and testing applications.
Security and Compliance Considerations
- Best practices for handling data in AI-driven features.
- Ensuring user privacy and obtaining consent within applications.
- Hands-on lab: Securing an AI feature.
Case Studies and Best Practices
- Examples of Gemini implementation in consumer and enterprise apps.
- Key lessons learned from real-world deployments.
- Best practices for building scalable AI features in applications.
Summary and Next Steps
Requirements
- Foundational programming knowledge in JavaScript, Kotlin, or Swift.
- Familiarity with the principles of mobile or web application development.
- Prior experience working with Firebase or cloud-based SDKs.
Target Audience
- Mobile application developers.
- Web application developers.
- Product management and development teams.
14 Hours
Testimonials (1)
easy steps in ML
