Deploying and Optimizing LLMs with Ollama Training Course
Ollama offers an efficient approach to deploying and running large language models (LLMs) locally or within production environments, granting users control over performance, costs, and security.
This instructor-led, live training (available online or on-site) targets intermediate-level professionals looking to deploy, optimize, and integrate LLMs using Ollama.
Upon completion of this training, participants will be able to:
- Set up and deploy LLMs using Ollama.
- Optimize AI models for peak performance and efficiency.
- Leverage GPU acceleration to enhance inference speeds.
- Integrate Ollama into existing workflows and applications.
- Monitor and maintain AI model performance over time.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request customized training for this course, please contact us to make arrangements.
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).
Target Audience
- AI engineers optimizing local and cloud-based AI deployments.
- Machine learning practitioners deploying and fine-tuning LLMs.
- DevOps specialists managing AI model integration.
Open Training Courses require 5+ participants.
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