Deploying and Optimizing LLMs with Ollama Training Course
Ollama offers a streamlined 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) is designed for intermediate-level professionals looking to deploy, optimize, and integrate LLMs using Ollama.
Upon completion of this training, participants will be able to:
- Configure and deploy LLMs using Ollama.
- Optimize AI models for enhanced performance and efficiency.
- Utilize GPU acceleration to improve inference speeds.
- Seamlessly integrate Ollama into existing workflows and applications.
- Monitor and sustain AI model performance over time.
Format of the Course
- Interactive lectures and discussions.
- Extensive exercises and practical activities.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request 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
- Foundational 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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