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Course Outline

Overview of Ollama Scalability

  • Architectural framework and scalability factors
  • Typical constraints in multi-user environments
  • Infrastructure preparation standards

Resource Management and GPU Efficiency

  • Strategies for optimal CPU and GPU utilization
  • Memory capacity and bandwidth requirements
  • Resource limits at the container level

Containerized Deployment and Kubernetes Integration

  • Docker-based containerization of Ollama
  • Implementation within Kubernetes clusters
  • Load distribution and service identification mechanisms

Autoscaling and Inference Batching

  • Formulating autoscaling guidelines for Ollama workloads
  • Batch processing techniques to enhance throughput
  • Balancing latency against throughput performance

Latency Reduction Strategies

  • Analyzing inference performance metrics
  • Data caching and model initialization protocols
  • M minimizing I/O and communication delays

Monitoring and System Observability

  • Prometheus integration for data collection
  • Grafana dashboard configuration for visualization
  • Alert protocols and incident management for Ollama systems

Expenditure Control and Scaling Approaches

  • Cost-optimized GPU provisioning
  • Evaluating cloud versus on-premises deployment options
  • Approaches for long-term scalable operations

Conclusion and Future Actions

Requirements

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

Audience

  • DevOps engineers
  • ML infrastructure teams
  • Site reliability engineers
 21 Hours

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