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Duration 21 hours
Course Outline
Foundations of Scaling Ollama
- Understanding Ollama’s architecture and key scaling factors
- Identifying common bottlenecks in multi-user setups
- Establishing best practices for infrastructure preparation
Resource Management and GPU Optimization
- Strategies for maximizing CPU and GPU utilization
- Considerations for memory and bandwidth management
- Defining resource constraints at the container level
Deployment via Containers and Kubernetes
- Packaging Ollama using Docker
- Deploying Ollama within Kubernetes clusters
- Implementing load balancing and service discovery
Autoscaling and Batch Processing
- Developing autoscaling policies tailored for Ollama
- Using batch inference to enhance throughput
- Balancing the trade-offs between latency and throughput
Reducing Latency
- Analyzing inference performance through profiling
- Employing caching strategies and model warm-up techniques
- Minimizing I/O and communication overheads
Monitoring and System Observability
- Integrating Prometheus for metrics collection
- Creating visual dashboards using Grafana
- Setting up alerting and incident response protocols for Ollama
Cost Control and Scalability Planning
- Allocating GPUs with a focus on cost efficiency
- Evaluating the pros and cons of cloud versus on-premises deployment
- Adopting strategies for sustainable long-term scaling
Conclusion and Future Directions
Requirements
- Proficiency in Linux system administration
- Solid understanding of containerization and orchestration principles
- Background in deploying machine learning models
Target Audience
- DevOps engineers
- ML infrastructure teams
- Site reliability engineers
