Getting Started with Ollama: Running Local AI Models

• Basic understanding of AI and machine learning concepts
• Familiarity with command-line interfaces

Audience

• Developers running AI models without cloud dependencies
• Business professionals interested in AI privacy and cost-effective deployment
• AI enthusiasts exploring local model deployment

Ollama is an open-source platform that allows users to run large language models (LLMs) locally without relying on cloud-based services.

This instructor-led, live training (online or onsite) is aimed at beginner-level professionals who wish to install, configure, and use Ollama for running AI models on their local machines.

By the end of this training, participants will be able to:

• Understand the fundamentals of Ollama and its capabilities.
• Set up Ollama for running local AI models.
• Deploy and interact with LLMs using Ollama.
• Optimize performance and resource usage for AI workloads.
• Explore use cases for local AI deployment in various industries.

Format of the Course

• Interactive lecture and discussion.
• Lots of 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.

Introduction to Ollama

• What is Ollama and how does it work?
• Benefits of running AI models locally
• Overview of supported LLMs (Llama, DeepSeek, Mistral, etc.)

Installing and Setting Up Ollama

• System requirements and hardware considerations
• Installing Ollama on different operating systems
• Configuring dependencies and environment setup

Running AI Models Locally

• Downloading and loading AI models in Ollama
• Interacting with models via the command line
• Basic prompt engineering for local AI tasks

Optimizing Performance and Resource Usage

• Managing hardware resources for efficient AI execution
• Reducing latency and improving model response time
• Benchmarking performance for different models

Use Cases for Local AI Deployment

• AI-powered chatbots and virtual assistants
• Data processing and automation tasks
• Privacy-focused AI applications

Summary and Next Steps