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Duration 21 hours
Course Outline
Introduction to Scaling Ollama
- Ollama’s architecture and key scaling considerations
- Identifying common bottlenecks in multi-user setups
- Best practices for preparing infrastructure
Resource Allocation and GPU Optimization
- Strategies for efficient CPU and GPU utilization
- Considerations for memory and bandwidth
- Applying resource constraints at the container level
Deployment with Containers and Kubernetes
- Containerizing Ollama using Docker
- Executing Ollama within Kubernetes clusters
- Implementing load balancing and service discovery
Autoscaling and Batching
- Developing autoscaling policies specific to Ollama
- Utilizing batch inference techniques to optimize throughput
- Balancing latency against throughput requirements
Latency Optimization
- Profiling inference performance metrics
- Employing caching strategies and model warm-up procedures
- Minimizing I/O and communication overhead
Monitoring and Observability
- Integrating Prometheus for metric collection
- Creating dashboards using Grafana
- Establishing alerting and incident response mechanisms for Ollama infrastructure
Cost Management and Scaling Strategies
- Cost-conscious GPU allocation methods
- Evaluating cloud versus on-premises deployment options
- Developing strategies for sustainable scaling
Summary and Next Steps
Requirements
- Proficiency in Linux system administration
- Knowledge of containerization and orchestration concepts
- Experience with deploying machine learning models
Target Audience
- DevOps Engineers
- ML Infrastructure Teams
- Site Reliability Engineers
