Introduction to Multimodal AI on Vertex AI
- What
is multimodal AI?
- Enterprise
use cases for multimodal systems
- Vertex
AI multimodal model ecosystem
- Benefits
and limitations
Vertex AI Multimodal Architecture
- Multimodal
model endpoints
- Data
flow for multimodal apps
- Authentication
and access control
- Cost
and scalability considerations
Text + Image Understanding
- Image
captioning and description
- Visual
question answering
- OCR
and document understanding
- Image
classification and tagging
Image Generation & Editing
- Text-to-image
generation
- Image
variations and inpainting
- Style
and brand control
- Quality
and resolution handling
Audio & Speech with Multimodal LLMs
- Speech-to-text
integration
- Text-to-speech
generation
- Voice-based
interactions
- Accessibility
use cases
Video Understanding & Generation
- Video
summarization and tagging
- Scene
and object understanding
- Storyboard-style
generation
- Video
pipeline workflows
Multimodal Prompt Engineering
- Designing
cross-modal prompts
- Aligning
text + visual context
- Controlling
outputs across modalities
- Iterative
refinement strategies
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
APIs and 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