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Duration 14 hours
Course Outline
Introduction to Mistral Multimodal Models
- An overview of Mistral Medium and its multimodal features.
- Exploration of OCR and document-specific models along with their applications.
- Strategies for integrating with open-source ecosystems.
OCR and Vision Pipelines
- Fundamentals of OCR using Mistral models.
- Techniques for preprocessing images and scanned documents.
- Methods for extracting structured text from visual data.
Document Understanding
- Structuring NLP pipelines for document processing.
- Implementing entity recognition, summarization, and classification tasks.
- Establishing cross-modal connections between text and vision data.
Search and Knowledge Applications
- Development of vision-text search systems.
- Constructing semantic search interfaces based on OCR outputs.
- Managing enterprise document repositories.
Assistive and Interactive Applications
- UI design considerations for multimodal assistants.
- Implementing accessibility solutions, such as vision-to-text conversion.
- Building real-world productivity tools.
Performance and Optimization
- Strategies for scaling multimodal pipelines.
- Tuning inference performance.
- Balancing accuracy against efficiency trade-offs.
Case Studies and Future Directions
- Review of industry applications involving multimodal AI.
- Analysis of current research trends in OCR and document AI.
- Discussion on responsible AI practices in vision-text tasks.
Summary and Next Steps
Requirements
- A solid grasp of natural language processing principles.
- Proficiency in Python and associated machine learning frameworks.
- Basic knowledge of computer vision concepts.
Target Audience
- Product teams
- ML researchers
- Applied ML engineers
