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Duration 14 hours
Course Outline
Introduction to Mistral Multimodal Models
- Exploring Mistral Medium and its multimodal features
- Examining OCR and document models through practical use cases
- Leveraging open-source ecosystems for integration
OCR and Vision Pipelines
- Core OCR principles utilizing Mistral models
- Techniques for preprocessing images and scanned documents
- Methods for extracting structured text from visual data
Document Understanding
- Structuring NLP pipelines specifically for document processing
- Implementing entity recognition, summarization, and classification
- Bridging text and vision data through cross-modal linking
Search and Knowledge Applications
- Architecting vision-text search systems
- Developing semantic search capabilities driven by OCR outputs
- Managing enterprise-scale document repositories
Assistive and Interactive Applications
- UI/UX design strategies for multimodal assistants
- Building accessibility tools, such as vision-to-text converters
- Enhancing real-world productivity with intelligent features
Performance and Optimization
- Strategies for scaling multimodal pipelines
- Optimizing inference performance for efficiency
- Balancing accuracy and efficiency in system design
Case Studies and Future Directions
- Reviewing industry implementations of multimodal AI
- Analyzing current research trends in OCR and document AI
- Addressing responsible AI practices in vision-text tasks
Summary and Next Steps
Requirements
- Foundational knowledge of natural language processing (NLP) concepts
- Proficiency with Python and common ML frameworks
- Basic understanding of computer vision principles
Target Audience
- Product development teams
- ML researchers
- Applied ML engineers
