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Duration 14 hours
Course Outline
Readying Machine Learning Models for Production Deployment
- Encapsulating models using Docker
- Model export processes from TensorFlow and PyTorch
- Strategies for versioning and model storage
Serving Models via Kubernetes
- Introduction to inference server architectures
- Implementation of TensorFlow Serving and TorchServe
- Configuration of model service endpoints
Optimizing Inference Performance
- Effective batching methodologies
- Management of concurrent requests
- Tuning for optimal latency and throughput
Dynamic Scaling for ML Workloads
- Utilization of the Horizontal Pod Autoscaler (HPA)
- Application of the Vertical Pod Autoscaler (VPA)
- Event-driven scaling via Kubernetes Event-Driven Autoscaling (KEDA)
GPU Allocation and Resource Governance
- Setup and configuration of GPU-enabled nodes
- Overview of the NVIDIA device plugin
- Defining resource requests and limits for ML workloads
Strategies for Model Rollout and Release
- Blue/green deployment techniques
- Application of canary rollout patterns
- A/B testing frameworks for model validation
Production Monitoring and Observability for ML
- Key metrics for inference workloads
- Best practices in logging and distributed tracing
- Designing dashboards and alerting systems
Ensuring Security and System Reliability
- Security measures for model endpoints
- Network policies and access control mechanisms
- Maintaining high availability standards
Conclusion and Future Directions
Requirements
- A solid grasp of containerized application workflows
- Practical experience with Python-based machine learning models
- Basic proficiency in Kubernetes concepts
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
