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Course Outline
Introduction to Mistral Multimodal Models
- Overview of Mistral Medium and its multimodal capabilities
- Exploring OCR and document models alongside their use cases
- Integration within open-source ecosystems
OCR and Vision Pipelines
- Core OCR principles using Mistral models
- Preprocessing techniques for images and scanned documents
- Extracting structured text from visual inputs
Document Understanding
- Designing NLP pipelines specifically for document processing
- Implementing entity recognition, summarization, and classification
- Linking text and vision data across modalities
Search and Knowledge Applications
- Developing vision-text search systems
- Constructing semantic search engines using OCR outputs
- Managing enterprise document repositories
Assistive and Interactive Applications
- Designing user interfaces for multimodal assistants
- Developing accessibility solutions (such as vision-to-text conversions)
- Creating practical productivity tools for real-world use
Performance and Optimization
- Scaling multimodal pipelines for efficiency
- Tuning inference performance
- Evaluating the balance between accuracy and efficiency
Case Studies and Future Directions
- Examining industry applications of multimodal AI
- Analyzing research trends in OCR and document AI
- Addressing responsible AI considerations in vision-text tasks
Summary and Next Steps
Requirements
- Knowledge of natural language processing concepts
- Proficiency in Python and machine learning frameworks
- Basic understanding of computer vision principles
Target Audience
- Product development teams
- Machine learning researchers
- Applied ML engineers
14 Hours
