Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 21 hours
Course Outline
Introduction to Ollama Scaling
- Examining 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/GPU utilization
- Considerations for memory and bandwidth management
- Applying container-level resource constraints
Deployment via Containers and Kubernetes
- Containerizing Ollama using Docker
- Executing Ollama within Kubernetes clusters
- Implementing load balancing and service discovery
Autoscaling and Batching
- Developing autoscaling policies tailored for Ollama
- Applying batch inference techniques to boost throughput
- Managing trade-offs between latency and throughput
Latency Optimization
- Profiling inference performance for insights
- Employing caching strategies and model warm-up processes
- Minimizing I/O and communication overhead
Monitoring and Observability
- Integrating Prometheus for metrics collection
- Creating dashboards using Grafana
- Setting up alerting and incident response for Ollama infrastructure
Cost Management and Scaling Strategies
- Implementing cost-aware GPU allocation
- Evaluating cloud versus on-premises deployment options
- Planning strategies for sustainable scaling
Summary and Next Steps
Requirements
- Hands-on experience in Linux system administration
- A solid grasp of containerization and orchestration concepts
- Knowledge of machine learning model deployment workflows
Target Audience
- DevOps engineers
- ML infrastructure specialists
- Site reliability engineers
