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Course Outline

Introduction to Scaling Ollama

  • Ollama’s architecture and scaling considerations.
  • Common bottlenecks in multi-user deployments.
  • Best practices for infrastructure readiness.

Resource Allocation and GPU Optimization

  • Strategies for efficient CPU/GPU utilization.
  • Memory and bandwidth considerations.
  • Container-level resource constraints.

Deployment with Containers and Kubernetes

  • Containerizing Ollama with Docker.
  • Running Ollama in Kubernetes clusters.
  • Load balancing and service discovery.

Autoscaling and Batching

  • Designing autoscaling policies for Ollama.
  • Batch inference techniques for throughput optimization.
  • Latency vs. throughput trade-offs.

Latency Optimization

  • Profiling inference performance.
  • Caching strategies and model warm-up.
  • Reducing I/O and communication overhead.

Monitoring and Observability

  • Integrating Prometheus for metrics.
  • Building dashboards with Grafana.
  • Alerting and incident response for Ollama infrastructure.

Cost Management and Scaling Strategies

  • Cost-aware GPU allocation.
  • Cloud vs. on-prem deployment considerations.
  • Strategies for sustainable scaling.

Summary and Next Steps

Requirements

  • Experience with Linux system administration.
  • Understanding of containerization and orchestration.
  • Familiarity with machine learning model deployment.

Audience

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

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