Multimodal Applications with Ollama Training Course
Ollama serves as a platform that facilitates the execution and fine-tuning of large language and multimodal models on local infrastructure.
This instructor-led live training, available either online or on-site, is designed for advanced machine learning engineers, AI researchers, and product developers who aim to create and deploy multimodal applications using Ollama.
Upon completion of this training, participants will be able to:
- Configure and execute multimodal models using Ollama.
- Integrate text, image, and audio inputs for practical, real-world applications.
- Construct systems for document understanding and visual question answering (VQA).
- Develop multimodal agents capable of performing reasoning across different data modalities.
Training Format
- Engaging lectures coupled with interactive discussions.
- Practical exercises utilizing real-world multimodal datasets.
- Live laboratory sessions for implementing multimodal pipelines via Ollama.
Customization Options
- For customized training needs, please contact us to arrange a tailored program.
Course Outline
Introduction to Multimodal AI and Ollama
- Overview of multimodal learning paradigms
- Key challenges in integrating vision and language
- Architecture and capabilities of Ollama
Setting Up the Ollama Environment
- Installation and configuration of Ollama
- Managing local model deployment
- Integrating Ollama with Python and Jupyter notebooks
Working with Multimodal Inputs
- Integrating text and image data
- Incorporating audio and structured data
- Designing effective preprocessing pipelines
Document Understanding Applications
- Extracting structured information from PDFs and images
- Combining OCR technology with language models
- Creating intelligent workflows for document analysis
Visual Question Answering (VQA)
- Preparing VQA datasets and benchmarks
- Training and evaluating multimodal models
- Developing interactive VQA applications
Designing Multimodal Agents
- Principles of agent design involving multimodal reasoning
- Unifying perception, language, and action
- Deploying agents for real-world use cases
Advanced Integration and Optimization
- Fine-tuning multimodal models using Ollama
- Optimizing inference performance
- Considerations for scalability and deployment
Summary and Next Steps
Requirements
- Solid grasp of machine learning principles
- Hands-on experience 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 working on workflows that combine vision and text processing
Open Training Courses require 5+ participants.
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