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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, along with their practical use cases
- Integration strategies within open-source ecosystems
OCR and Vision Pipelines
- OCR fundamentals when working with Mistral models
- Preprocessing techniques for images and scanned documents
- Methods for extracting structured text from visual inputs
Document Understanding
- Designing NLP pipelines optimized for document processing
- Implementing entity recognition, summarization, and classification
- Linking text and vision data across modalities
Search and Knowledge Applications
- Architecting vision-text search systems
- Constructing semantic search engines leveraging OCR outputs
- Managing enterprise document repositories
Assistive and Interactive Applications
- UI design principles for multimodal assistants
- Accessibility applications, such as vision-to-text conversion
- Developing real-world productivity tools
Performance and Optimization
- Strategies for scaling multimodal pipelines
- Tuning inference performance for efficiency
- Balancing accuracy and efficiency trade-offs
Case Studies and Future Directions
- Industry applications of multimodal AI
- Emerging research trends in OCR and document AI
- Ethical and responsible AI considerations in vision-text tasks
Summary and Next Steps
Requirements
- Foundational knowledge of natural language processing concepts.
- Proficiency in Python and machine learning frameworks.
- Basic familiarity with computer vision principles.
Target Audience
- Product teams
- ML researchers
- Applied ML engineers
