Ollama Multimodal AI Training is a practical training program designed to help professionals build and use multimodal AI applications using locally hosted models with Ollama. The course focuses on working with text, images, and combined inputs to create intelligent applications while maintaining data privacy and offline capabilities. Participants will learn how multimodal models operate and how to design effective workflows without relying on cloud-based or paid AI tools.

• Understand multimodal AI concepts and applications
• Use Ollama for text, image, and multimodal processing
• Build multimodal AI workflows and applications
• Integrate local AI models with business use cases
• Evaluate multimodal AI performance and outputs

• Developers and engineers
• AI practitioners and researchers
• Product and solution architects
• IT and infrastructure professionals
• Organizations adopting local AI solutions

• Basic computer and system usage skills
• Familiarity with AI or LLM fundamentals
• No prior multimodal or deep learning experience required

• Understanding multimodal AI concepts and workflows
• Using Ollama for text and image-based AI tasks
• Designing prompts for multimodal interactions
• Building privacy-first multimodal applications
• Evaluating outputs from multimodal AI models

By the end of this training, participants will be able to design and run multimodal AI applications using Ollama that combine text and image inputs effectively. Learners will gain practical skills to build secure, privacy-first multimodal workflows suitable for real-world use cases.

Introduction to Multimodal AI
• Understanding multimodal models and use cases
• Text, image, and cross-modal interactions
• Advantages of local multimodal AI deployments

Overview of Ollama Multimodal Capabilities
• Multimodal models supported by Ollama
• System requirements and performance considerations
• Use cases for private and offline multimodal AI

Getting Started with Multimodal Models in Ollama
• Installing and running multimodal models
• Handling text and image inputs
• Understanding response formats and limitations

Prompting Techniques for Multimodal Applications
• Designing prompts for image understanding
• Combining text and visual context effectively
• Improving clarity and accuracy in multimodal outputs

Building Multimodal Use Cases
• Image analysis and interpretation
• Visual question answering
• Content generation using text and images

Multimodal Workflow Design
• Structuring multimodal tasks and pipelines
• Creating reusable prompts and templates
• Managing consistency across multimodal interactions

Performance, Accuracy, and Error Handling
• Handling hallucinations and misinterpretations
• Optimizing prompts for reliable outputs
• Understanding model limitations and trade-offs

Security, Ethics, and Responsible Multimodal AI
• Privacy considerations with image data
• Ethical use of visual and textual information
• Responsible deployment of multimodal systems

Hands-on Multimodal Application Exercises
• Real-world multimodal scenarios
• Guided prompt and workflow experimentation
• Participant practice with feedback

• Multimodal AI fundamentals
• Ollama multimodal model setup
• Text and image processing workflows
• AI integration and application development
• Performance evaluation and optimization

• Participation in hands-on multimodal exercises
• Prompt and workflow-based assignments
• Scenario-driven practical assessment

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training and evaluation will receive an AcadNXT Certificate of Completion in Ollama Multimodal AI Training, validating their skills in building multimodal applications using Ollama.