Multimodal Applications with Ollama Training Course
Ollama is a platform designed to facilitate the running and fine-tuning of large language and multimodal models locally.
This instructor-led, live training (available online or on-site) is targeted at advanced ML engineers, AI researchers, and product developers who aim to build and deploy multimodal applications using Ollama.
By the end of this training, participants will be able to:
- Set up and operate multimodal models with Ollama.
- Integrate text, image, and audio inputs for practical applications.
- Create systems for document understanding and visual question answering.
- Develop multimodal agents capable of reasoning across different types of data.
Format of the Course
- Interactive lectures and discussions.
- Hands-on practice with real multimodal datasets.
- Live 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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