Course Code: dollmsollama
Duration: 14 hours
Prerequisites:
  • 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
Overview:

Ollama provides an efficient way to deploy and run large language models (LLMs) locally or in production environments, offering control over performance, cost, and security.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to deploy, optimize, and integrate LLMs using Ollama.

By the end 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.
  • 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.
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

Sites Published:

United Arab Emirates - Deploying and Optimizing LLMs with Ollama

Qatar - Deploying and Optimizing LLMs with Ollama

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