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Duration 21 hours
Course Outline
Foundations of Ollama Scalability
- Architectural components and scalability factors
- Identifying performance constraints in multi-user configurations
- Ensuring infrastructure readiness for operational efficiency
Resource Management and GPU Efficiency
- Strategies for optimizing CPU and GPU utilization
- Evaluating memory and bandwidth requirements
- Defining resource limits at the container level
Containerized Deployment and Orchestration
- Implementing Ollama within Docker containers
- Deploying workloads on Kubernetes clusters
- Configuring load balancing and service discovery mechanisms
Dynamic Scaling and Batch Processing
- Establishing autoscaling policies for Ollama services
- Applying batch inference methods to enhance throughput
- Balancing latency requirements against processing volume
Latency Reduction Techniques
- Analyzing inference performance metrics
- Implementing caching protocols and model initialization procedures
- Minimizing input/output and network communication delays
Performance Monitoring and Oversight
- Integrating Prometheus for data collection
- Creating visualization dashboards using Grafana
- Establishing alerting systems and incident response protocols
Fiscal Responsibility and Growth Planning
- Allocating GPU resources with budgetary considerations
- Assessing cloud versus on-premises hosting options
- Developing sustainable expansion strategies for government agencies
Conclusion and Future Directions
Requirements
- Proficiency in Linux system administration
- Knowledge of containerization and orchestration principles
- Experience with machine learning model deployment
Intended Audience
- DevOps engineers
- Machine learning infrastructure specialists
- Site reliability engineers