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Duration 21 hours
Course Outline
Foundations of Ollama Scaling
- Analyzing Ollama’s architecture and its implications for scaling
- Identifying typical bottlenecks in multi-user setups
- Establishing best practices for infrastructure preparation
Resource Management and GPU Tuning
- Strategies for maximizing CPU and GPU efficiency
- Addressing memory and network bandwidth constraints
- Defining resource limits at the container level
Containerized Deployment with Kubernetes
- Encapsulating Ollama within Docker containers
- Executing Ollama across Kubernetes clusters
- Implementing load balancing and service discovery mechanisms
Autoscaling and Batching Mechanisms
- Formulating autoscaling policies tailored to Ollama
- Utilizing batch inference to boost throughput
- Balancing latency requirements against throughput goals
Reducing Latency
- Analyzing inference performance through profiling
- Implementing caching and model warm-up procedures
- Minimizing I/O and inter-service communication overhead
Monitoring and System Observability
- Connecting Prometheus for metric collection
- Developing visualization dashboards using Grafana
- Configuring alerts and incident response protocols for Ollama
Cost Control and Scalability Planning
- Allocating GPUs with a focus on cost efficiency
- Evaluating cloud versus on-premises deployment options
- Developing strategies for sustainable long-term scaling
Conclusion and Future Directions
Requirements
- Proficiency in Linux system administration
- Comprehensive understanding of containerization and orchestration principles
- Exposure to machine learning model deployment workflows
Target Audience
- DevOps Engineers
- ML Infrastructure Specialists
- Site Reliability Engineers
