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Duration 14 hours
Course Outline
Introduction to Mistral Multimodal Models
- Exploring Mistral Medium and its multimodal capabilities
- OCR and document models: key use cases
- Leveraging integration with open-source ecosystems
OCR and Vision Pipelines
- Fundamentals of OCR using Mistral models
- Image and scanned document preprocessing techniques
- Extracting structured text from visual inputs
Document Understanding
- Designing effective NLP pipelines for documents
- Entity recognition, summarization, and classification workflows
- Linking text and vision data across modalities
Search and Knowledge Applications
- Implementing vision-text search systems
- Creating semantic search features using OCR outputs
- Managing enterprise document repositories
Assistive and Interactive Applications
- UI design principles for multimodal assistants
- Accessibility-focused applications (e.g., vision-to-text)
- Developing real-world productivity tools
Performance and Optimization
- Scaling strategies for multimodal pipelines
- Tuning inference performance
- Balancing accuracy and efficiency trade-offs
Case Studies and Future Directions
- Industry examples of multimodal AI implementation
- Emerging research trends in OCR and document AI
- Responsible AI practices in vision-text tasks
Summary and Next Steps
Requirements
- A solid grasp of natural language processing principles
- Proficiency in Python and mainstream ML frameworks
- Basic knowledge of computer vision concepts
Target Audience
- Product development teams
- ML researchers
- Applied ML engineers
