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 Duration 21 hours

Course Outline

Introduction to Scaling Ollama

  • Overview of Ollama’s architecture and scaling factors
  • Typical bottlenecks in multi-user deployments
  • Best practices for ensuring infrastructure readiness

Resource Allocation and GPU Optimization

  • Strategies for efficient CPU and GPU utilization
  • Key considerations for memory and bandwidth
  • Resource constraints at the container level

Deployment with Containers and Kubernetes

  • Containerizing Ollama using Docker
  • Executing Ollama within Kubernetes clusters
  • Load balancing and service discovery mechanisms

Autoscaling and Batching

  • Developing autoscaling policies specific to Ollama
  • Batch inference methods to enhance throughput
  • Balancing latency and throughput trade-offs

Latency Optimization

  • Analyzing inference performance
  • Implementing caching strategies and model warm-up
  • Minimizing I/O and communication overhead

Monitoring and Observability

  • Integrating Prometheus for metric collection
  • Creating dashboards using Grafana
  • Alerting and incident response for Ollama infrastructure

Cost Management and Scaling Strategies

  • Cost-aware GPU allocation methods
  • Considerations for cloud versus on-prem deployment
  • Strategies for sustainable scaling

Summary and Next Steps

Requirements

  • Practical experience in Linux system administration
  • Comprehension of containerization and orchestration principles
  • Knowledge of machine learning model deployment

Target Audience

  • DevOps engineers
  • ML infrastructure teams
  • Site reliability engineers

Custom Corporate Training

Training solutions designed exclusively for businesses.

  • Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
  • Flexible Schedule: Dates and times adapted to your team's agenda.
  • Format: Online (live), In-company (at your offices), or Hybrid.
Investment

Price per private group, online live training, starting from 4800 € + VAT*

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