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)

Audience

  • AI engineers optimizing local and cloud-based AI deployments
  • ML practitioners deploying and fine-tuning LLMs
  • DevOps specialists managing AI model integration
 14 Hours

Delivery Options

Private Group Training

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    • Practical hands-on exercises containing data / scenarios recognisable to the learners
  • Training scheduled on a date of your choice
  • Delivered online, onsite/classroom or hybrid by experts sharing real world experience

Private Group Prices RRP from £3800 online delivery, based on a group of 2 delegates, £1200 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.

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