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 settings, granting you full control over performance, costs, and security.
This instructor-led, live training (available online or onsite) is designed for intermediate-level professionals looking to deploy, optimise, and integrate LLMs using Ollama.
By the end of this training, participants will be able to:
- Set up and deploy LLMs using Ollama.
- Optimise AI models for peak performance and efficiency.
- Leverage GPU acceleration to enhance inference speeds.
- Seamlessly integrate Ollama into existing workflows and applications.
- Monitor and maintain AI model performance over time.
Format of the Course
- Interactive lectures and discussions.
- Ample exercises and practical practice.
- Hands-on implementation in a live-lab environment.
Course Customisation Options
- To request a customised 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
- Dockerising 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
Optimising 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 optimising 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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