Multimodal Applications with Ollama Training Course
Ollama serves as a platform designed for executing and fine-tuning large language and multimodal models directly on local infrastructure.
This instructor-led live training, available either online or at your site, targets advanced ML engineers, AI researchers, and product developers who aim to construct and deploy multimodal applications utilizing Ollama.
Upon completion of this training, participants will be equipped to:
- Configure and execute multimodal models using Ollama.
- Unify text, image, and audio inputs for practical application scenarios.
- Construct systems for document comprehension and visual question answering.
- Create multimodal agents capable of reasoning across different data modalities.
Course Format
- Engaging lectures and interactive discussions.
- Practical exercises using real-world multimodal datasets.
- Live laboratory sessions for implementing multimodal pipelines via Ollama.
Customization Options
- To arrange a tailored training session for this course, please contact us.
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
- Solid grasp of machine learning principles
- Proficiency with deep learning frameworks like PyTorch or TensorFlow
- Knowledge of natural language processing and computer vision
Target Audience
- Machine learning engineers
- AI researchers
- Product developers integrating text and vision workflows
Open Training Courses require 5+ participants.
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