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Course Outline

Introduction to Multimodal Artificial Intelligence and Ollama

  • Overview of multimodal learning frameworks
  • Key challenges in vision-language integration
  • Capabilities and architecture of Ollama

Setting Up the Ollama Environment for Government Operations

  • Installing and configuring Ollama
  • Working with local model deployment
  • Integrating Ollama with Python and Jupyter

Working with Multimodal Inputs

  • Text and image integration
  • Incorporating audio and structured data
  • Designing preprocessing pipelines

Document Understanding Applications for Government

  • Extracting structured information from PDFs and images
  • Combining OCR with language models
  • Building intelligent document analysis workflows

Visual Question Answering (VQA)

  • Setting up VQA datasets and benchmarks
  • Training and evaluating multimodal models
  • Building interactive VQA applications

Designing Multimodal Agents

  • Principles of agent design with multimodal reasoning
  • Combining perception, language, and action
  • Deploying agents for real-world use cases

Advanced Integration and Optimization

  • Fine-tuning multimodal models with Ollama
  • Optimizing inference performance
  • Scalability and deployment considerations

Summary and Next Steps

Requirements

  • Demonstrated competency in foundational machine learning principles
  • Proficiency with deep learning environments, including PyTorch or TensorFlow
  • Working knowledge of natural language processing and computer vision applications

Intended Beneficiaries

  • Machine learning engineering personnel
  • Artificial intelligence researchers
  • Software developers implementing integrated text and visual data workflows for government
 21 Hours

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