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Course Outline
Introduction to Ollama for LLM Deployment
- Overview of Ollama’s core capabilities
- Benefits of local AI model deployment
- Comparison against cloud-based AI hosting solutions
Setting Up the Deployment Environment
- Installation of Ollama and essential dependencies
- Configuration of hardware and GPU acceleration
- Containerizing Ollama for scalable deployments
Deploying LLMs with Ollama
- Loading and managing AI models
- Deploying models such as Llama 3, DeepSeek, and Mistral
- Creating APIs and endpoints for model access
Optimizing LLM Performance
- Fine-tuning models for operational efficiency
- Reducing latency and enhancing response times
- Managing memory usage and resource allocation
Integrating Ollama into AI Workflows
- Connecting Ollama to applications and services
- Automating AI-driven business processes
- Implementing Ollama in edge computing scenarios
Monitoring and Maintenance
- Performance tracking and issue debugging
- Updating and managing AI models
- Maintaining security and compliance in AI deployments
Scaling AI Model Deployments
- Best practices for managing high workloads
- Scaling Ollama for enterprise-level use cases
- Exploring future advancements in local AI deployment
Summary and Next Steps
Requirements
- Fundamental experience with machine learning and AI models
- Proficiency with command-line interfaces and scripting
- Knowledge of deployment environments, including local, edge, and cloud
Audience
- AI engineers focused on optimizing local and cloud-based AI deployments
- ML practitioners responsible for deploying and fine-tuning LLMs
- DevOps specialists managing AI model integration
14 Hours
