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Duration 21 hours
Course Outline
Foundations of Multimodal AI and Ollama
- Introduction to multimodal learning concepts
- Primary challenges in vision-language integration
- Ollama's core capabilities and architectural design
Establishing the Ollama Environment
- Installation and configuration of Ollama
- Managing local model deployment
- Seamless integration of Ollama with Python and Jupyter
Handling Multimodal Data Inputs
- Combining text and image data streams
- Including audio and structured data formats
- Architecting efficient preprocessing pipelines
Applications in Document Understanding
- Extracting structured insights from PDFs and images
- Merging OCR technologies with language models
- Creating sophisticated document analysis workflows
Visual Question Answering (VQA)
- Implementing VQA datasets and evaluation benchmarks
- Training and assessing multimodal model performance
- Developing interactive VQA user experiences
Architecting Multimodal Agents
- Core principles of agent design with multimodal reasoning
- Synthesizing perception, language, and action
- Deploying agents for practical industry use cases
Advanced Integration and Performance Tuning
- Fine-tuning multimodal models using Ollama
- Enhancing inference speed and efficiency
- Addressing scalability and deployment strategies
Recap and Future Directions
Requirements
- Proficiency in fundamental machine learning principles
- Hands-on 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 integrating vision and text workflows
