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Duration 21 hours
Course Outline
Foundations of Scaling Ollama
- Overview of Ollama’s architecture and key scaling factors
- Identifying common bottlenecks in multi-user setups
- Essential best practices for preparing infrastructure
Resource Allocation & GPU Tuning
- Strategies for maximizing CPU/GPU efficiency
- Key considerations for memory and bandwidth
- Applying resource constraints at the container level
Deployment via Containers & Kubernetes
- Packaging Ollama using Docker
- Operating Ollama within Kubernetes clusters
- Implementing load balancing and service discovery
Autoscaling & Batch Processing
- Crafting autoscaling policies specific to Ollama
- Utilizing batch inference to boost throughput
- Balancing latency against throughput requirements
Latency Reduction
- Analyzing inference performance through profiling
- Implementing caching and model warm-up techniques
- Minimizing I/O and communication overhead
Monitoring & Observability
- Setting up Prometheus for metrics collection
- Creating visual dashboards with Grafana
- Establishing alerting and incident response protocols for Ollama infrastructure
Cost Management & Scaling Strategies
- Allocating GPUs with cost efficiency in mind
- Evaluating cloud versus on-prem deployment options
- Planning for long-term, sustainable scalability
Conclusion & Path Forward
Requirements
- Proficiency in Linux system administration
- Working knowledge of containerization and orchestration
- Background in deploying machine learning models
Target Audience
- DevOps Engineers
- ML Infrastructure Teams
- Site Reliability Engineers
