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Duration 21 hours
Course Outline
Introduction to Multimodal AI and Ollama
- Fundamentals of multimodal learning
- Primary challenges in integrating vision and language
- Ollama’s capabilities and architectural overview
Configuring the Ollama Environment
- Installation and setup of Ollama
- Strategies for local model deployment
- Connecting Ollama with Python and Jupyter environments
Handling Multimodal Inputs
- Merging text and image data
- Including audio and structured data sources
- Architecting effective preprocessing pipelines
Document Understanding Applications
- Extracting structured data from PDFs and images
- Synergizing OCR with language models
- Creating workflows for intelligent document analysis
Visual Question Answering (VQA)
- Establishing VQA datasets and benchmarks
- Training and assessing multimodal models
- Building interactive VQA solutions
Designing Multimodal Agents
- Core principles of agent design with multimodal reasoning
- Unifying perception, language, and action
- Rolling out agents for practical use cases
Advanced Integration and Optimization
- Fine-tuning multimodal models using Ollama
- Enhancing inference performance
- Addressing scalability and deployment strategies
Summary and Next Steps
Requirements
- Solid grasp of core machine learning principles
- Practical experience with deep learning frameworks like PyTorch or TensorFlow
- Working knowledge of natural language processing and computer vision
Target Audience
- Machine learning engineers
- AI researchers
- Product developers implementing workflows that integrate vision and text
