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Course Outline

Preparing Machine Learning Models for Deployment

  • Packaging models using Docker
  • Exporting models from TensorFlow and PyTorch
  • Versioning and storage considerations

Model Serving on Kubernetes

  • An overview of inference servers
  • Deploying TensorFlow Serving and TorchServe
  • Configuring model endpoints

Inference Optimization Techniques

  • Batching strategies
  • Managing concurrent requests
  • Tuning for latency and throughput

Autoscaling ML Workloads

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

GPU Provisioning and Resource Management

  • Configuring GPU nodes
  • NVIDIA device plugin overview
  • 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
  • Logging and tracing practices
  • Dashboards and alerting

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
  • Familiarity with the fundamentals of Kubernetes

Audience

  • ML Engineers
  • DevOps Engineers
  • Platform Engineering Teams
 14 Hours

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