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Duration 21 hours
Course Outline
Foundations of Ollama Scaling
- Examining Ollama’s architectural design and key scaling factors
- Identifying typical bottlenecks in multi-user environments
- Establishing best practices for infrastructure preparation
Resource Management and GPU Efficiency
- Strategies for maximizing CPU and GPU utilization
- Key considerations for memory and network bandwidth
- Defining resource constraints at the container level
Containerized Deployment with Kubernetes
- Packaging Ollama using Docker
- Deploying Ollama within Kubernetes clusters
- Implementing load balancing and service discovery mechanisms
Autoscaling and Batch Processing
- Formulating autoscaling policies tailored for Ollama
- Utilizing batch inference to enhance throughput
- Balancing the trade-offs between latency and throughput
Minimizing Latency
- Analyzing inference performance through profiling
- Employing caching techniques and model warm-up procedures
- Mitigating I/O and communication overhead
Monitoring and System Observability
- Connecting Prometheus for comprehensive metrics collection
- Creating visual dashboards using Grafana
- Establishing alerting systems and incident response protocols for Ollama infrastructure
Cost Control and Scalability Planning
- Implementing cost-conscious GPU allocation methods
- Evaluating deployment strategies for cloud versus on-premise environments
- Developing strategies for sustainable long-term scaling
Recap and Future Directions
Requirements
- Proficiency in Linux system administration
- Solid grasp of containerization and orchestration principles
- Knowledge of machine learning model deployment workflows
Intended Audience
- DevOps Engineers
- ML Infrastructure Teams
- Site Reliability Engineers
