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Course Outline

Introduction to Mistral Multimodal Models

  • Insight into Mistral Medium and its multimodal features
  • OCR and document models along with their practical uses
  • Connecting with open-source ecosystems

OCR and Vision Pipelines

  • Core OCR concepts utilizing Mistral models
  • Processing images and scanned documents
  • Deriving structured text from visual inputs

Document Comprehension

  • Creating NLP pipelines for document processing
  • Performing entity recognition, summarization, and classification
  • Establishing cross-modal connections between text and visual data

Search and Knowledge Systems

  • Implementing vision-text search mechanisms
  • Constructing semantic search engines using OCR results
  • Managing enterprise document repositories

Assistive and Interactive Solutions

  • Designing UIs for multimodal assistants
  • Accessibility tools (such as vision-to-text conversion)
  • Practical real-world productivity utilities

Performance and Optimization

  • Scaling multimodal pipelines for larger workloads
  • Tuning inference performance
  • Assessing the balance between accuracy and efficiency

Case Studies and Future Trends

  • Industrial applications of multimodal AI
  • Current research trends in OCR and document AI
  • Ethical and responsible AI practices in vision-text tasks

Conclusion and Subsequent Steps

Requirements

  • A solid grasp of natural language processing principles
  • Proficiency in Python and machine learning frameworks
  • Basic knowledge of computer vision concepts

Target Audience

  • Product development teams
  • ML researchers
  • Applied ML engineers
 14 Hours

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