Multimodal Applications with Ollama Training Course
Ollama serves as a platform that facilitates the execution and fine-tuning of large language and multimodal models on local infrastructure.
This instructor-led live training (available online or onsite) is designed for advanced ML engineers, AI researchers, and product developers looking to construct and deploy multimodal applications using Ollama.
Upon completing this training, participants will be equipped to:
- Configure and operate multimodal models via Ollama.
- Combine text, image, and audio inputs for practical applications.
- Create systems for document comprehension and visual question answering.
- Develop multimodal agents capable of reasoning across different data types.
Course Format
- Interactive lectures and discussions.
- Practical exercises using real multimodal datasets.
- Live lab sessions implementing multimodal pipelines with Ollama.
Customization Options
- For customized training requests, please contact us to arrange the details.
Course Outline
Introduction to Multimodal AI and Ollama
- Overview of multimodal learning
- Key challenges in vision-language integration
- Capabilities and architecture of Ollama
Setting Up the Ollama Environment
- 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
- 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
- Strong grasp of machine learning concepts
- Experience with deep learning frameworks like PyTorch or TensorFlow
- Familiarity with natural language processing and computer vision
Target Audience
- Machine learning engineers
- AI researchers
- Product developers integrating vision and text workflows
Open Training Courses require 5+ participants.
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