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Duration 14 hours
Course Outline
Introduction to Mistral Multimodal Models
- Overview of Mistral Medium and its multimodal features
- OCR/document models and practical use cases
- Integration with open-source ecosystems
OCR and Vision Pipelines
- Fundamentals of OCR using Mistral models
- Preprocessing images and scanned documents
- Extracting structured text from visual content
Document Understanding
- Architecting NLP pipelines for document processing
- Entity recognition, summarization, and classification techniques
- Cross-modal linking of textual and visual data
Search and Knowledge Applications
- Vision-text search systems
- Creating semantic search engines using OCR outputs
- Managing enterprise document repositories
Assistive and Interactive Applications
- UI design strategies for multimodal assistants
- Accessibility applications (e.g., vision-to-text conversion)
- Real-world productivity tool development
Performance and Optimization
- Scaling multimodal pipelines
- Tuning inference performance
- Balancing accuracy and efficiency trade-offs
Case Studies and Future Directions
- Industry applications of multimodal AI
- Research trends in OCR and document AI
- Responsible AI considerations in vision-text tasks
Summary and Next Steps
Requirements
- A solid grasp of natural language processing concepts
- Hands-on experience with Python and ML frameworks
- Basic knowledge of computer vision principles
Target Audience
- Product teams
- ML researchers
- Applied ML engineers
