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Duration 14 hours
Course Outline
Introduction to Mistral Multimodal Models
- Overview of Mistral Medium and its multimodal features.
- Exploration of OCR and document models, including practical use cases.
- Integration strategies within open-source ecosystems.
OCR and Vision Pipelines
- Core OCR concepts utilizing Mistral models.
- Techniques for preprocessing images and scanned documents.
- Methods for extracting structured text from visual data.
Document Understanding
- Architecting NLP pipelines specifically for document processing.
- Implementing entity recognition, summarization, and classification tasks.
- Linking text and vision data across modalities.
Search and Knowledge Applications
- Designing vision-text search systems.
- Creating semantic search engines based on OCR outputs.
- Managing enterprise-level document repositories.
Assistive and Interactive Applications
- UI design principles for multimodal assistants.
- Accessibility solutions, such as vision-to-text conversion.
- Development of real-world productivity tools.
Performance and Optimization
- Strategies for scaling multimodal pipelines.
- Tuning inference performance for optimal results.
- Balancing accuracy against efficiency in model evaluations.
Case Studies and Future Directions
- Real-world industry applications of multimodal AI.
- Current research trends in OCR and document AI.
- Ethical and responsible AI considerations in vision-text tasks.
Summary and Next Steps
Requirements
- A solid grasp of natural language processing concepts.
- Proficiency in Python and common ML frameworks.
- Basic familiarity with computer vision principles.
Target Audience
- Product teams.
- ML researchers.
- Applied ML engineers.
