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Course Outline
Introduction to Scaling Ollama for Government
- Ollama’s architecture and scaling considerations for government environments
- Common bottlenecks in multi-user deployments within public sector workflows
- Best practices for infrastructure readiness to support government operations
Resource Allocation and GPU Optimization for Government
- Strategies for efficient CPU/GPU utilization tailored for government systems
- Memory and bandwidth considerations in a public sector context
- Container-level resource constraints to ensure compliance with government standards
Deployment with Containers and Kubernetes for Government
- Containerizing Ollama with Docker to meet government security requirements
- Running Ollama in Kubernetes clusters optimized for government infrastructure
- Load balancing and service discovery tailored for government operations
Autoscaling and Batching for Government
- Designing autoscaling policies for Ollama to support dynamic government workloads
- Batch inference techniques for throughput optimization in government applications
- Latency vs. throughput trade-offs in a public sector environment
Latency Optimization for Government
- Profiling inference performance to enhance government service delivery
- Caching strategies and model warm-up techniques for government systems
- Reducing I/O and communication overhead in public sector deployments
Monitoring and Observability for Government
- Integrating Prometheus for metrics to ensure transparency and accountability in government operations
- Building dashboards with Grafana to support data-driven decision-making for government
- Alerting and incident response mechanisms for Ollama infrastructure in a public sector context
Cost Management and Scaling Strategies for Government
- Cost-aware GPU allocation strategies to optimize government budgets
- Cloud vs. on-prem deployment considerations tailored for government needs
- Strategies for sustainable scaling to support long-term government goals
Summary and Next Steps for Government
Requirements
- Experience in Linux system administration for government environments
- Understanding of containerization and orchestration technologies
- Familiarity with the deployment of machine learning models
Audience
- DevOps engineers for government agencies
- Machine learning infrastructure teams for government projects
- Site reliability engineers for government operations
21 Hours
