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 Duration 21 hours

Course Outline

Foundations of Ollama Scalability

  • Architectural components and scalability factors
  • Identifying performance constraints in multi-user configurations
  • Ensuring infrastructure readiness for operational efficiency

Resource Management and GPU Efficiency

  • Strategies for optimizing CPU and GPU utilization
  • Evaluating memory and bandwidth requirements
  • Defining resource limits at the container level

Containerized Deployment and Orchestration

  • Implementing Ollama within Docker containers
  • Deploying workloads on Kubernetes clusters
  • Configuring load balancing and service discovery mechanisms

Dynamic Scaling and Batch Processing

  • Establishing autoscaling policies for Ollama services
  • Applying batch inference methods to enhance throughput
  • Balancing latency requirements against processing volume

Latency Reduction Techniques

  • Analyzing inference performance metrics
  • Implementing caching protocols and model initialization procedures
  • Minimizing input/output and network communication delays

Performance Monitoring and Oversight

  • Integrating Prometheus for data collection
  • Creating visualization dashboards using Grafana
  • Establishing alerting systems and incident response protocols

Fiscal Responsibility and Growth Planning

  • Allocating GPU resources with budgetary considerations
  • Assessing cloud versus on-premises hosting options
  • Developing sustainable expansion strategies for government agencies

Conclusion and Future Directions

Requirements

  • Proficiency in Linux system administration
  • Knowledge of containerization and orchestration principles
  • Experience with machine learning model deployment

Intended Audience

  • DevOps engineers
  • Machine learning infrastructure specialists
  • Site reliability engineers

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