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Course Outline
Introduction to Mistral Multimodal Models
- Overview of Mistral Medium and its multimodal capabilities
- OCR and document models, along with their practical use cases
- Integration with open-source ecosystems
OCR and Vision Pipelines
- Foundations of OCR using Mistral models
- Preprocessing images and scanned documents
- Extracting structured text from visual inputs
Document Understanding
- Designing NLP pipelines for document processing
- Implementing entity recognition, summarisation, and classification
- Establishing cross-modal links between text and vision data
Search and Knowledge Applications
- Developing vision-text search systems
- Building semantic search capabilities using OCR outputs
- Managing enterprise document repositories
Assistive and Interactive Applications
- UI design principles for multimodal assistants
- Accessibility applications, such as vision-to-text conversion
- Practical real-world productivity tools
Performance and Optimisation
- Scaling multimodal pipelines for high demand
- Tuning inference performance
- Balancing accuracy and efficiency trade-offs
Case Studies and Future Directions
- Industry applications of multimodal AI
- Emerging research trends in OCR and document AI
- Considerations for responsible AI in vision-text tasks
Summary and Next Steps
Requirements
- A solid grasp of natural language processing concepts.
- Hands-on experience with Python and ML frameworks.
- Basic familiarity with computer vision principles.
Target Audience
- Product teams.
- ML researchers.
- Applied ML engineers.
14 Hours
