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Course Outline

Introduction to Ollama for Large Language Model Deployment

  • Capabilities and functional scope of Ollama
  • Benefits of hosting AI models within local infrastructure
  • Evaluation of local hosting versus cloud-based AI services for government

Establishing the Deployment Infrastructure

  • Installation of Ollama and associated system dependencies
  • Configuration of hardware resources and GPU acceleration
  • Implementation of containerization via Docker for scalable operations

Execution of LLM Deployments Using Ollama

  • Procedures for loading and managing AI model assets
  • Deployment of Llama 3, DeepSeek, Mistral, and compatible models
  • Establishment of application programming interfaces (APIs) for secure model access

Performance Optimization of LLMs

  • Model fine-tuning for enhanced operational efficiency
  • Strategies for latency reduction and improved response times
  • Management of system memory and resource allocation

Integration of Ollama into Agency Workflows

  • Connectivity between Ollama and existing agency applications and services
  • Automation of AI-assisted operational processes
  • Utilization of Ollama in edge computing environments for government

Monitoring and System Maintenance

  • Performance tracking and troubleshooting methodologies
  • Protocols for model updates and lifecycle management
  • Adherence to security standards and regulatory compliance in AI deployments

Scaling AI Model Deployments

  • Best practices for managing high-volume workloads
  • Scaling Ollama infrastructure for enterprise-grade requirements
  • Emerging trends in local AI model deployment technologies

Summary and Strategic Next Steps

Requirements

  • Foundational knowledge of artificial intelligence and machine learning frameworks
  • Proficiency in command-line operations and automation scripting
  • Comprehension of infrastructure contexts, including on-premises, edge, and cloud ecosystems

Intended Audience

  • Engineering professionals optimizing AI workloads across hybrid environments
  • Machine learning specialists responsible for the deployment and refinement of large language models
  • Systems administration personnel overseeing the integration of AI capabilities into enterprise workflows

This resource is designed for government entities seeking to enhance technical capabilities in AI infrastructure management.

 14 Hours

Number of participants


Price per participant

Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.

Upcoming Courses

Deploying and Optimizing LLMs with Ollama

2026-07-20 09:30
14 hours
Virginia Beach, VA – Regus at Windwood Center
$ 2703 (Online)
$ 3903 (Classroom)

Deploying and Optimizing LLMs with Ollama

2026-08-03 09:30
14 hours
Chicago, IL - CBD - West Loop Riverside Plaza Center
$ 2703 (Online)
$ 3903 (Classroom)

Deploying and Optimizing LLMs with Ollama

2026-08-17 09:30
14 hours
Peoria, IL - Regus at 416 Main St
$ 2703 (Online)
$ 3903 (Classroom)

Deploying and Optimizing LLMs with Ollama

2026-08-31 09:30
14 hours
Springfield, IL
$ 2703 (Online)
$ 3903 (Classroom)

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