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Duration 21 hours
Course Outline
Introduction to Ollama Scaling
- Ollama's architectural components and scaling considerations
- Identifying common bottlenecks in multi-user deployments
- Best practices for ensuring infrastructure readiness
Resource Allocation and GPU Optimization
- Strategies for efficient CPU and GPU utilization
- Considerations regarding memory and bandwidth
- Managing resource constraints at the container level
Deployment with Containers and Kubernetes
- Containerizing Ollama using Docker
- Running Ollama within Kubernetes clusters
- Implementing load balancing and service discovery
Autoscaling and Batching
- Developing autoscaling policies tailored for Ollama
- Batch inference techniques to enhance throughput
- Balancing latency against throughput requirements
Latency Optimization
- Profiling inference performance metrics
- Caching strategies and model warm-up techniques
- Minimizing I/O and communication overhead
Monitoring and Observability
- Integrating Prometheus for metrics collection
- Constructing dashboards using Grafana
- Setting up alerting and incident response for Ollama infrastructure
Cost Management and Scaling Strategies
- Cost-effective GPU allocation methods
- Evaluating cloud versus on-premises deployment options
- Strategies for maintaining sustainable scaling
Summary and Next Steps
Requirements
- Practical experience in Linux system administration
- A solid grasp of containerization and orchestration principles
- Knowledge of machine learning model deployment processes
Target Audience
- DevOps engineers
- ML infrastructure teams
- Site reliability engineers
