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Course Outline
Introduction to Ollama for LLM Deployment
- Overview of Ollama’s core capabilities
- Benefits of deploying AI models locally
- Comparison against cloud-based AI hosting solutions
Configuring the Deployment Environment
- Installing Ollama and essential dependencies
- Setting up hardware and enabling GPU acceleration
- Containerizing Ollama using Docker for scalable deployments
Deploying LLMs via Ollama
- Loading and managing various AI models
- Deploying models such as Llama 3, DeepSeek, and Mistral
- Creating APIs and endpoints for AI model accessibility
Optimizing LLM Performance
- Fine-tuning models for improved efficiency
- Minimizing latency and enhancing response times
- Managing memory usage and resource allocation
Integrating Ollama into AI Workflows
- Connecting Ollama to external applications and services
- Automating AI-driven business processes
- Utilizing Ollama within edge computing environments
Monitoring and Maintenance
- Tracking performance metrics and troubleshooting issues
- Updating and managing AI model versions
- Maintaining security and compliance standards in AI deployments
Scaling AI Model Deployments
- Best practices for managing high workloads
- Scaling Ollama for enterprise-level use cases
- Future trends in local AI model deployment
Summary and Next Steps
Requirements
- Fundamental experience with machine learning and AI models
- Proficiency in command-line interfaces and scripting
- Knowledge of deployment environments, including local, edge, and cloud
Target Audience
- AI engineers focused on optimizing local and cloud-based AI deployments
- ML practitioners engaged in deploying and fine-tuning LLMs
- DevOps specialists responsible for managing AI model integration
14 Hours
