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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
- Establishing best practices for infrastructure preparation
Resource Management and GPU Efficiency
- Strategies for optimal CPU/GPU utilization
- Considerations for memory and bandwidth management
- Defining resource constraints at the container level
Containerized Deployment and Kubernetes
- Encapsulating Ollama using Docker
- Executing Ollama within Kubernetes clusters
- Implementing load balancing and service discovery
Autoscaling Mechanisms and Batching
- Formulating autoscaling policies specific to Ollama
- Utilizing batch inference techniques to boost throughput
- Balancing latency against throughput requirements
Enhancing Latency Performance
- Analyzing inference performance via profiling
- Implementing caching methods and model warm-up procedures
- Minimizing I/O and communication overhead
System Monitoring and Observability
- Connecting Prometheus for metric collection
- Creating visual dashboards using Grafana
- Setting up alerting systems and incident response for Ollama infrastructure
Cost Control and Scalability Planning
- Allocating GPUs with cost efficiency in mind
- Evaluating cloud versus on-premises deployment options
- Adopting strategies for sustainable long-term scaling
Conclusion and Future Actions
Requirements
- Proficiency in Linux system administration
- Knowledge of containerization and orchestration concepts
- Experience with deploying machine learning models
Target Audience
- DevOps engineers
- ML infrastructure teams
- Site reliability engineers
