Multimodal Applications with Ollama Training Course
Ollama is a platform that enables running and fine-tuning large language and multimodal models locally.
This instructor-led, live training (online or onsite) is aimed at advanced-level ML engineers, AI researchers, and product developers who wish to build and deploy multimodal applications with Ollama.
By the end of this training, participants will be able to:
- Set up and run multimodal models with Ollama.
- Integrate text, image, and audio inputs for real-world applications.
- Build document understanding and visual QA systems.
- Develop multimodal agents capable of reasoning across modalities.
Format of the Course
- Interactive lecture and discussion.
- Hands-on practice with real multimodal datasets.
- Live-lab implementation of multimodal pipelines using Ollama.
Course Customization Options
- To request a customized training 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
- Strong understanding of machine learning concepts
- Experience with deep learning frameworks such as PyTorch or TensorFlow
- Familiarity with natural language processing and computer vision
Audience
- Machine learning engineers
- AI researchers
- Product developers integrating vision and text workflows
Open Training Courses require 5+ participants.
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