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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 their practical applications
- Integration within open-source ecosystems
OCR and Vision Pipelines
- Foundations of OCR using Mistral models
- Preprocessing techniques for images and scanned documents
- Extraction of structured text from visual data
Document Understanding
- Designing NLP pipelines for document processing
- Entity recognition, summarization, and classification techniques
- Linking text and vision data across modalities
Search and Knowledge Applications
- Building vision-text search systems
- Implementing semantic search using OCR outputs
- Managing enterprise document repositories
Assistive and Interactive Applications
- UI design for multimodal assistants
- Accessibility-focused applications (e.g., vision-to-text conversion)
- Development of real-world productivity tools
Performance and Optimization
- Scaling multimodal pipelines for production
- Optimizing inference performance
- Assessing trade-offs between accuracy and efficiency
Case Studies and Future Directions
- Industry-wide applications of multimodal AI
- Emerging research trends in OCR and document AI
- Ethical and responsible AI considerations in vision-text tasks
Summary and Next Steps
Requirements
- Familiarity with natural language processing concepts
- Proficiency with Python and machine learning frameworks
- Basic knowledge of computer vision principles
Target Audience
- Product teams
- ML researchers
- Applied ML engineers
