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 Duration 14 hours

Course Outline

Preparing Machine Learning Models for Deployment

  • Packaging models using Docker
  • Exporting models from TensorFlow and PyTorch
  • Considerations for versioning and storage

Serving Models on Kubernetes

  • Introduction to inference servers
  • Deploying TensorFlow Serving and TorchServe
  • Establishing model endpoints

Techniques for Optimizing Inference

  • Strategies for batching
  • Handling concurrent requests
  • Tuning for latency and throughput

Autoscaling ML Workloads

  • Horizontal Pod Autoscaler (HPA)
  • Vertical Pod Autoscaler (VPA)
  • Kubernetes Event-Driven Autoscaling (KEDA)

Provisioning GPUs and Managing Resources

  • Configuration of GPU nodes
  • Overview of the NVIDIA device plugin
  • Defining resource requests and limits for ML workloads

Model Rollout and Release Strategies

  • Blue/green deployments
  • Canary rollout patterns
  • A/B testing for model evaluation

Monitoring and Observability for ML in Production

  • Metrics for inference workloads
  • Best practices for logging and tracing
  • Dashboards and alerting systems

Security and Reliability Considerations

  • Securing model endpoints
  • Network policies and access control
  • Ensuring high availability

Summary and Next Steps

Requirements

  • A solid grasp of containerized application workflows
  • Practical experience with Python-based machine learning models
  • Basic familiarity with Kubernetes concepts

Target Audience

  • ML engineers
  • DevOps engineers
  • Platform engineering teams

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