Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Introduction to Ollama for LLM Deployment
- Overview of Ollama’s core capabilities.
- Benefits of deploying AI models locally.
- Comparison with cloud-based AI hosting solutions.
Setting Up the Deployment Environment
- Installing Ollama and necessary dependencies.
- Configuring hardware and enabling GPU acceleration.
- Containerizing Ollama with Docker for scalable deployments.
Deploying LLMs with Ollama
- Loading and managing various AI models.
- Deploying models such as Llama 3, DeepSeek, and Mistral.
- Creating APIs and endpoints for AI model access.
Optimizing LLM Performance
- Fine-tuning models for operational efficiency.
- Minimizing 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 processes.
- Implementing Ollama in edge computing environments.
Monitoring and Maintenance
- Tracking performance metrics and debugging issues.
- Updating and managing AI models.
- Ensuring security and compliance 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 with 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.
