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Duration 21 hours
Course Outline
Introduction to Ollama Scaling
- Ollama’s architecture and key scaling considerations
- Identifying common bottlenecks in multi-user deployments
- Best practices for ensuring infrastructure readiness
Resource Allocation and GPU Optimization
- Strategies for efficient CPU/GPU utilization
- Considerations for memory and bandwidth
- Managing container-level resource constraints
Deployment with Containers and Kubernetes
- Containerizing Ollama using Docker
- Operating Ollama within Kubernetes clusters
- Implementing load balancing and service discovery
Autoscaling and Batching
- Developing autoscaling policies for Ollama
- Batch inference techniques for improving throughput
- Balancing latency against throughput
Latency Optimization
- Profiling inference performance
- Implementing caching strategies and model warm-up
- Minimizing I/O and communication overhead
Monitoring and Observability
- Integrating Prometheus for metrics collection
- Creating dashboards using Grafana
- Establishing alerting and incident response mechanisms for Ollama infrastructure
Cost Management and Scaling Strategies
- Cost-aware GPU allocation
- Evaluating cloud versus on-prem deployment options
- Strategies for sustainable scaling
Summary and Next Steps
Requirements
- Experience in Linux system administration
- Comprehensive understanding of containerization and orchestration
- Proficiency in deploying machine learning models
Target Audience
- DevOps engineers
- ML infrastructure teams
- Site reliability engineers
