Deploying and Optimizing LLMs with Ollama Training Course
Ollama provides an efficient method for deploying and running large language models (LLMs) either locally or in production environments, granting users control over performance, costs, and security.
This instructor-led, live training (available online or onsite) is designed for intermediate-level professionals who aim to deploy, optimize, and integrate LLMs using Ollama.
By the conclusion of this training, participants will be able to:
- Set up and deploy LLMs using Ollama.
- Optimize AI models for performance and efficiency.
- Leverage GPU acceleration for improved inference speeds.
- Integrate Ollama into workflows and applications.
- Monitor and maintain AI model performance over time.
Format of the Course
- Interactive lecture and discussion.
- Extensive exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction to Ollama for LLM Deployment
- Overview of Ollama’s capabilities
- Advantages of local AI model deployment
- Comparison with cloud-based AI hosting solutions
Setting Up the Deployment Environment
- Installing Ollama and required dependencies
- Configuring hardware and GPU acceleration
- Dockerizing 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
- Basic experience with machine learning and AI models
- Familiarity with command-line interfaces and scripting
- Understanding of deployment environments (local, edge, cloud)
Audience
- AI engineers optimizing local and cloud-based AI deployments
- ML practitioners deploying and fine-tuning LLMs
- DevOps specialists managing AI model integration
Open Training Courses require 5+ participants.
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