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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 practical use cases
- Integration strategies within open-source ecosystems
OCR and Vision Pipelines
- Core principles of OCR using Mistral models
- Techniques for preprocessing images and scanned documents
- Methods for extracting structured text from visual inputs
Document Understanding
- Architecting NLP pipelines for comprehensive document analysis
- Implementation of entity recognition, summarization, and classification
- Linking text and vision data across modalities
Search and Knowledge Applications
- Designing vision-text search systems
- Building semantic search engines utilizing OCR outputs
- Managing enterprise document repositories
Assistive and Interactive Applications
- Designing user interfaces for multimodal assistants
- Developing accessibility solutions, such as vision-to-text conversion
- Creating real-world productivity enhancements
Performance and Optimization
- Strategies for scaling multimodal pipelines
- Optimizing inference performance
- Balancing accuracy against efficiency in model evaluation
Case Studies and Future Directions
- Real-world industry applications of multimodal AI
- Current research trends in OCR and document AI
- Considerations for responsible AI in vision-text tasks
Summary and Next Steps
Requirements
- Fundamental understanding of Natural Language Processing (NLP) concepts
- Proficiency in Python and experience with ML frameworks
- Basic familiarity with computer vision principles
Target Audience
- Product development teams
- ML researchers
- Applied ML engineers
