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Duration 14 hours
Course Outline
Getting ML Models Ready for Deployment
- Packaging models using Docker
- Exporting models from TensorFlow and PyTorch
- Considerations for versioning and storage
Serving Models on Kubernetes
- An introduction to inference servers
- Deployment of TensorFlow Serving and TorchServe
- Configuration of 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)
GPU Provisioning and Resource Oversight
- Configuration of GPU nodes
- Overview of the NVIDIA device plugin
- Setting resource requests and limits for ML workloads
Strategies for Model Rollout and Release
- Blue/green deployment techniques
- Canary rollout patterns
- A/B testing for model evaluation
Monitoring and Observability for Production ML
- Key metrics for inference workloads
- Best practices for logging and tracing
- Setup of dashboards and alerting systems
Security and Reliability Focus
- Securing model endpoints
- Implementing network policies and access control
- Maintaining high availability
Summary and Next Steps
Requirements
- A solid understanding of containerized application workflows
- Practical experience with Python-based machine learning models
- Familiarity with the fundamentals of Kubernetes
Target Audience
- ML engineers
- DevOps engineers
- Platform engineering teams
Testimonials (2)
As i said before , for a person like me (no exp. ) this was a gateway to understanding features and functions with these programs/tools & etc. .
Patrick V. Duylovski - UBB + DZI (KBC GROUP)
Course - Docker and Kubernetes
basic understanding of container/kubernetes and how they interact features of the openshift plattform
