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Duration 21 hours
Course Outline
Foundations of Scaling Ollama
- Overview of Ollama’s architecture and scaling factors
- Identifying bottlenecks in multi-user deployments
- Best practices for ensuring infrastructure readiness
Resource Allocation and GPU Optimization
- Strategies for maximizing CPU and GPU utilization
- Considerations for memory and bandwidth management
- Implementing container-level resource limits
Deployment via Containers and Kubernetes
- Encapsulating Ollama using Docker
- Operating Ollama within Kubernetes clusters
- Managing load balancing and service discovery
Autoscaling and Batch Processing
- Formulating autoscaling policies for Ollama
- Applying batch inference methods to boost throughput
- Navigating the balance between latency and throughput
Latency Reduction Techniques
- Analyzing inference performance metrics
- Implementing caching and model warm-up protocols
- Minimizing I/O and communication overhead
Monitoring and Observability
- Connecting Prometheus for metric collection
- Creating visual dashboards with Grafana
- Establishing alerting and incident response for Ollama infrastructure
Cost Control and Scaling Methodologies
- Implementing cost-effective GPU allocation
- Evaluating cloud versus on-premises deployment scenarios
- Developing sustainable scaling strategies
Conclusion and Future Directions
Requirements
- Proficiency in Linux system administration
- Comprehension of containerization and orchestration
- Knowledge of machine learning model deployment
Target Audience
- DevOps engineers
- ML infrastructure teams
- Site reliability engineers
