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Duration 14 hours
Course Outline
Overview of Mistral Multimodal Models
- Introduction to Mistral Medium and its multimodal features
- Applications of OCR and document-specific models
- Connecting with open-source developer ecosystems
OCR and Vision Processing Pipelines
- Core OCR concepts when using Mistral models
- Techniques for preprocessing images and scanned documents
- Methods for extracting structured text from visual data
Document Intelligence
- Structuring NLP workflows for document analysis
- Implementing entity recognition, summarization, and classification
- Linking text and visual data across modalities
Search and Knowledge-Base Systems
- Developing vision-text search architectures
- Creating semantic search interfaces using OCR data
- Managing enterprise document repositories
Assistive and Interactive Interfaces
- Designing UIs for multimodal AI assistants
- Building accessibility tools such as vision-to-text converters
- Implementing real-world productivity enhancements
Performance Optimization
- Scaling multimodal processing workflows
- Tuning inference performance
- Assessing the balance between accuracy and efficiency
Case Studies and Emerging Trends
- Industry implementations of multimodal AI
- Current research directions in OCR and document AI
- Ethical and responsible AI practices in vision-text domains
Key Takeaways and Future Steps
Requirements
- A solid grasp of natural language processing principles.
- Proficiency in Python and various ML frameworks.
- Basic knowledge of computer vision concepts.
Target Audience
- Product development teams.
- Machine learning researchers.
- Applied ML engineers.
