Gemini 3 Multimodal Apps Training in New York, USA

The Gemini 3 Multimodal Apps Training is a hands-on program that teaches how to build applications using Google Gemini 3 multimodal capabilities, enabling apps that understand and generate across text, images, audio, and video.

The course focuses on API integration, multimodal prompt design, workflow orchestration, and production deployment, helping teams create scalable, reliable, and enterprise-grade multimodal AI applications.

Introduction to Multimodal AI with Gemini 3

• What is multimodal AI?
• Gemini 3 multimodal capabilities
• Enterprise use cases
• Benefits and limitations

Gemini 3 Multimodal Architecture

• Gemini 3 API endpoints
• Data flow for multimodal apps
• Authentication and access control
• Cost and scalability considerations

Text + Image Understanding

• Visual question answering
• Image description and captioning
• OCR and document image understanding
• Image classification and tagging

Image Generation & Editing

• Text-to-image generation
• Image variations and inpainting
• Style and brand control
• Resolution and quality handling

Audio & Speech in Multimodal Apps

• Speech-to-text integration
• Text-to-speech generation
• Voice-based interactions
• Accessibility use cases

Video Understanding & Generation

• Video summarization
• Scene and object detection
• Storyboard-style generation
• Video pipeline workflows

Multimodal Prompt Engineering

• Designing cross-modal prompts
• Aligning text + visual context
• Controlling outputs across modalities
• Iterative prompt refinement

Multimodal Workflow Orchestration

• Chaining multimodal model calls
• Combining vision + language + audio
• Conditional and branching workflows
• State and context management

Structured Outputs & Validation

• Enforcing structured multimodal outputs
• JSON schemas for multimodal tasks
• Validation and error handling
• Reliability patterns

Safety, Moderation & Policy Controls

• Content safety for images, audio, and video
• Moderation filters
• Brand safety and compliance
• Responsible AI for multimodal content

Performance, Cost & Scaling

• Latency optimization
• Batch vs real-time processing
• Caching and reuse
• Cost monitoring and limits

Integration with Applications

• Web and mobile app integration
• Backend service patterns
• CMS and DAM integration
• API gateway and security patterns

Testing & Quality Assurance

• Evaluating multimodal outputs
• Consistency and brand checks
• Automated QA strategies
• Human review workflows

Monitoring & Observability

• Tracking multimodal requests
• Logging and tracing
• Drift and quality monitoring
• Alerting and troubleshooting

Hands-on Exercises

Summary and Conclusion