Multimodal Applications with Ollama Training Course
Ollama is a platform designed to facilitate the execution and fine-tuning of large language and multimodal models directly on your local infrastructure.
This instructor-led live training session, available either online or on-site, is tailored for advanced ML engineers, AI researchers, and product developers who aim to construct and deploy multimodal applications leveraging Ollama.
Upon completion of this training, participants will be equipped to:
- Configure and operate multimodal models via Ollama.
- Combine text, image, and audio inputs for practical, real-world applications.
- Create systems for document understanding and visual question answering.
- Develop multimodal agents capable of reasoning across different data modalities.
Course Format
- Interactive lectures and group discussions.
- Practical exercises using authentic multimodal datasets.
- Live laboratory implementation of multimodal pipelines with Ollama.
Customization Options
- For tailored training solutions for this course, please contact us to arrange.
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
- Robust understanding of machine learning principles
- Practical experience with deep learning frameworks such as PyTorch or TensorFlow
- Familiarity with natural language processing and computer vision techniques
Target Audience
- Machine learning engineers
- AI researchers
- Product developers integrating vision and text workflows
Open Training Courses require 5+ participants.
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