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Course Outline
Introduction to Mistral Multimodal Models
- Overview of Mistral Medium and its multimodal features
- OCR and document models: applications and use cases
- Integration with open-source ecosystems
OCR and Vision Pipelines
- OCR fundamentals using Mistral models
- Image and scanned document preprocessing
- Extraction of structured text from visual inputs
Document Understanding
- Architecture of NLP pipelines for document processing
- Entity recognition, summarization, and classification tasks
- Cross-modal connection between text and vision data
Search and Knowledge Applications
- Vision-text search system design
- Semantic search implementation using OCR outputs
- Management of enterprise document repositories
Assistive and Interactive Applications
- UI design principles for multimodal assistants
- Accessibility solutions (e.g., vision-to-text conversion)
- Practical productivity tool development
Performance and Optimization
- Scalability strategies for multimodal pipelines
- Inference performance tuning and optimization
- Assessing the balance between accuracy and efficiency
Case Studies and Future Directions
- Real-world industry applications of multimodal AI
- Evolving research trends in OCR and document AI
- Responsible AI practices in vision-text tasks
Summary and Next Steps
Requirements
- A solid grasp of natural language processing concepts
- Proficiency with Python and common ML frameworks
- Basic knowledge of computer vision principles
Target Audience
- Product teams
- ML researchers
- Applied ML engineers
14 Hours