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