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Duration 14 hours
Course Outline
Introduction to Mistral Multimodal Models
- Overview of Mistral Medium and its multimodal features
- Exploration of OCR and document models with relevant use cases
- Integration strategies within open-source ecosystems
OCR and Vision Pipelines
- Fundamentals of OCR using Mistral models
- Techniques for preprocessing images and scanned documents
- Making structured text extraction from visual data
Document Understanding
- Architecting NLP pipelines for document processing
- Implementing entity recognition, summarization, and classification
- Establishing cross-modal connections between text and vision data
Search and Knowledge Applications
- Developing vision-text search systems
- Creating semantic search interfaces leveraging OCR outputs
- Building enterprise document repositories
Assistive and Interactive Applications
- UI design principles for multimodal assistants
- Developing accessibility tools (such as vision-to-text conversion)
- Creating real-world productivity utilities
Performance and Optimization
- Strategies for scaling multimodal pipelines
- Tuning inference performance
- Assessing the trade-offs between accuracy and efficiency
Case Studies and Future Directions
- Examining industry applications of multimodal AI
- Exploring research trends in OCR and document AI
- Considering Responsible AI aspects in vision-text tasks
Summary and Next Steps
Requirements
- A solid grasp of natural language processing concepts
- Practical experience with Python and machine learning frameworks
- Basic knowledge of computer vision principles
Target Audience
- Product development teams
- ML researchers
- Applied ML engineers
