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Duration 21 hours
Course Outline
Foundations of Ollama Scaling
- Understanding Ollama's architecture and key scaling factors
- Identifying typical bottlenecks in multi-user setups
- Establishing best practices for infrastructure preparedness
Resource Management and GPU Efficiency
- Strategies for maximizing CPU and GPU utilization
- Evaluating memory and bandwidth requirements
- Applying resource constraints at the container level
Deployment via Containers and Kubernetes
- Encapsulating Ollama using Docker
- Executing Ollama within Kubernetes clusters
- Implementing load balancing and service discovery
Autoscaling and Batch Processing
- Formulating autoscaling policies specific to Ollama
- Applying batch inference methods to boost throughput
- Balancing latency against throughput goals
Reducing Latency
- Analyzing inference performance through profiling
- Employing caching techniques and model warm-up procedures
- Minimizing I/O and communication overhead
Monitoring and System Observability
- Connecting Prometheus for metrics collection
- Creating visual dashboards with Grafana
- Setting up alerts and incident response protocols for Ollama infrastructure
Financial Management and Growth Strategies
- Allocating GPU resources with cost awareness
- Weighing cloud versus on-premises deployment factors
- Developing strategies for sustainable expansion
Conclusion and Forward Path
Requirements
- Proficiency in Linux system administration
- Working knowledge of containerization and orchestration principles
- Experience with deploying machine learning models
Target Audience
- DevOps engineers
- Machine learning infrastructure teams
- Site reliability engineers
