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Duration 14 hours
Course Outline
Introduction to Mistral Multimodal Models
- Exploring Mistral Medium and its multimodal potential
- OCR and document models: capabilities and use cases
- Integrating with open-source ecosystems
OCR and Vision Pipelines
- OCR fundamentals utilizing Mistral models
- Image and scanned document preprocessing techniques
- Extracting structured text from visual data
Document Understanding
- Designing NLP pipelines for document processing
- Entity recognition, summarization, and classification
- Linking text and vision data across modalities
Search and Knowledge Applications
- Implementing vision-text search systems
- Creating semantic search capabilities from OCR outputs
- Managing enterprise document repositories
Assistive and Interactive Applications
- UI design principles for multimodal assistants
- Accessibility solutions (e.g., vision-to-text conversion)
- Practical productivity tools for real-world use
Performance and Optimization
- Scaling multimodal pipelines for efficiency
- Tuning inference performance
- Balancing accuracy and efficiency in evaluations
Case Studies and Future Directions
- Industrial applications of multimodal AI
- Emerging 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
- Practical experience with Python and ML frameworks
- Basic familiarity with computer vision principles
Target Audience
- Product teams
- ML researchers
- Applied ML engineers
