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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/document models and their practical applications.
- Integration strategies within open-source ecosystems.
OCR and Vision Pipelines
- Foundations of OCR when using Mistral models.
- Techniques for preprocessing images and scanned documents.
- Methods for extracting structured text from visual data.
Document Understanding
- Designing effective NLP pipelines for document processing.
- Implementing entity recognition, summarization, and classification tasks.
- Linking text and vision data across modalities.
Search and Knowledge Applications
- Developing vision-text search systems.
- Creating semantic search engines powered by OCR outputs.
- Managing enterprise document repositories.
Assistive and Interactive Applications
- UI design principles for multimodal assistants.
- Applications for accessibility, such as vision-to-text conversion.
- Building real-world productivity tools.
Performance and Optimization
- Strategies for scaling multimodal pipelines.
- Tuning inference performance for optimal results.
- Balancing accuracy and efficiency trade-offs.
Case Studies and Future Directions
- Industry examples of multimodal AI in practice.
- 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 standard ML frameworks.
- Basic familiarity with computer vision principles.
Intended Audience
- Product development teams.
- ML researchers.
- Applied ML engineers.
