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Duration 21 hours
Course Outline
Introduction to Scaling Ollama
- Overview of 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 and GPU utilization.
- Considerations regarding memory and bandwidth.
- Managing resource constraints at the container level.
Deployment with Containers and Kubernetes
- Containerizing Ollama using Docker.
- Deploying Ollama within Kubernetes clusters.
- Implementing load balancing and service discovery.
Autoscaling and Batching
- Designing autoscaling policies tailored for Ollama.
- Applying batch inference techniques to enhance throughput.
- Navigating the trade-offs between latency and throughput.
Latency Optimization
- Profiling inference performance.
- Employing 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.
- Establishing alerting mechanisms and incident response protocols for Ollama infrastructure.
Cost Management and Scaling Strategies
- Cost-aware approaches to GPU allocation.
- Evaluating cloud versus on-premises deployment options.
- Developing strategies for sustainable scaling.
Summary and Next Steps
Requirements
- Proficiency in Linux system administration.
- A solid grasp of containerization and orchestration concepts.
- Familiarity with deploying machine learning models.
Target Audience
- DevOps Engineers.
- ML Infrastructure Teams.
- Site Reliability Engineers.
