Multimodal Applications with Ollama Training Course
Ollama serves as a platform that allows users to run and fine-tune large language models and multimodal models on their own hardware.
This guided, live training session (available online or at your location) is designed for experienced ML engineers, AI researchers, and product developers who want to create and launch multimodal applications using Ollama.
Upon completing this training, participants will be able to:
- Configure and operate multimodal models using Ollama.
- Combine 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 data types.
Course Format
- Engaging lectures and interactive discussions.
- Practical exercises using real multimodal datasets.
- Live laboratory work implementing multimodal pipelines with Ollama.
Customization Options
- To arrange a tailored training version of this course, please contact us.
Course Outline
Introduction to Multimodal AI and Ollama
- Understanding the fundamentals of multimodal learning
- Addressing key challenges in vision-language integration
- Exploring Ollama's capabilities and architecture
Setting Up the Ollama Environment
- Installing and configuring Ollama
- Deploying models locally
- Integrating Ollama with Python and Jupyter
Handling Multimodal Inputs
- Merging text and image data
- Incorporating audio and structured data
- Designing effective preprocessing pipelines
Applications for Document Understanding
- Extracting structured information from PDFs and images
- Combining OCR technology with language models
- Creating intelligent document analysis workflows
Visual Question Answering (VQA)
- Preparing VQA datasets and benchmarks
- Training and evaluating multimodal models
- Building interactive VQA applications
Designing Multimodal Agents
- Core principles of agent design with multimodal reasoning
- Merging perception, language processing, and action capabilities
- Deploying agents for real-world scenarios
Advanced Integration and Optimization
- Fine-tuning multimodal models with Ollama
- Enhancing inference performance
- Considering scalability and deployment strategies
Summary and Next Steps
Requirements
- Strong grasp of machine learning principles
- Hands-on experience with deep learning frameworks like PyTorch or TensorFlow
- Familiarity with natural language processing and computer vision techniques
Target Audience
- Machine learning engineers
- AI researchers
- Product developers incorporating vision and text workflows
Open Training Courses require 5+ participants.
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