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Course Outline
Introduction to Ollama for LLM Deployment
- Overview of Ollama’s capabilities
- Benefits of local AI model deployment
- Comparison with cloud-based AI hosting solutions
Setting Up the Deployment Environment
- Installing Ollama and necessary dependencies
- Configuring hardware and GPU acceleration
- Containerizing Ollama for scalable deployments
Deploying LLMs with Ollama
- Loading and managing AI models
- Deploying Llama 3, DeepSeek, Mistral, and other models
- Creating APIs and endpoints for AI model access
Optimizing LLM Performance
- Fine-tuning models for efficiency
- Reducing latency and improving response times
- Managing memory and resource allocation
Integrating Ollama into AI Workflows
- Connecting Ollama to applications and services
- Automating AI-driven processes
- Using Ollama in edge computing environments
Monitoring and Maintenance
- Tracking performance and debugging issues
- Updating and managing AI models
- Ensuring security and compliance in AI deployments
Scaling AI Model Deployments
- Best practices for handling high workloads
- Scaling Ollama for enterprise use cases
- Future advancements in local AI model deployment
Summary and Next Steps
Requirements
- Foundational experience with machine learning and AI models
- Proficiency in command-line interfaces and scripting
- Knowledge of deployment environments (local, edge, cloud)
Target Audience
- AI engineers focused on optimizing local and cloud-based AI deployments
- Machine learning practitioners deploying and fine-tuning LLMs
- DevOps specialists responsible for managing AI model integration
14 Hours
