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Duration 21 hours
Course Outline
Introduction to Ollama Scaling
- Overview of Ollama’s architecture and scaling factors
- Identifying common bottlenecks in multi-user setups
- Best practices for preparing infrastructure
Resource Allocation and GPU Optimization
- Strategies for efficient CPU and GPU usage
- Considerations for memory and bandwidth management
- Managing resource constraints at the container level
Deployment via Containers and Kubernetes
- Encapsulating Ollama using Docker
- Executing Ollama within Kubernetes clusters
- Implementing load balancing and service discovery
Autoscaling and Batching
- Creating autoscaling policies tailored for Ollama
- Techniques for batch inference to boost throughput
- Balancing latency against throughput
Latency Optimization
- Analyzing inference performance
- Implementing caching strategies and model warm-up
- Minimizing I/O and communication overhead
Monitoring and Observability
- Using Prometheus for metrics integration
- Creating dashboards with Grafana
- Setting up alerting and incident response for Ollama infrastructure
Cost Management and Scaling Strategies
- Cost-conscious GPU allocation
- Evaluating cloud versus on-premises deployment options
- Strategies for sustainable scalability
Summary and Future Steps
Requirements
- Proficiency in Linux system administration
- Knowledge of containerization and orchestration concepts
- Understanding of machine learning model deployment processes
Target Audience
- DevOps engineers
- ML infrastructure teams
- Site reliability engineers
