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Duration 14 hours
Course Outline
Introduction to Mistral Multimodal Models
- Overview of Mistral Medium and its multimodal capabilities
- OCR and document models: practical use cases
- Integration within open-source ecosystems
OCR and Vision Pipelines
- Foundations of OCR using Mistral models
- Preprocessing techniques for images and scanned documents
- Extracting structured text from visual data
Document Understanding
- Architecting NLP pipelines for document processing
- Entity recognition, summarization, and classification tasks
- Establishing cross-modal links between text and vision data
Search and Knowledge Applications
- Designing vision-text search systems
- Creating semantic search functionalities using OCR outputs
- Managing enterprise document repositories
Assistive and Interactive Applications
- UI design strategies for multimodal assistants
- Accessibility features, such as vision-to-text conversion
- Developing real-world productivity tools
Performance and Optimization
- Scaling multimodal pipeline infrastructure
- Tuning inference performance
- Balancing accuracy and efficiency trade-offs
Case Studies and Future Directions
- Real-world industry applications of multimodal AI
- Emerging research trends in OCR and document AI
- Ethical and responsible AI practices in vision-text tasks
Summary and Next Steps
Requirements
- A solid grasp of natural language processing principles
- Proficiency in Python and machine learning frameworks
- Foundational knowledge of computer vision
Target Audience
- Product development teams
- ML researchers
- Applied ML engineers
