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Duration 14 hours
Course Outline
Preparing Machine Learning Models for Production
- Packaging models using Docker
- Exporting models from TensorFlow and PyTorch
- Considerations for versioning and model storage
Serving Models on Kubernetes
- Introduction to inference servers
- Deploying TensorFlow Serving and TorchServe
- Establishing model endpoints
Optimizing Inference Performance
- Implementing 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 Allocation
- Configuring GPU-enabled nodes
- Overview of the NVIDIA device plugin
- Defining resource requests and limits for ML workloads
Model Release and Rollout Strategies
- Blue/green deployment patterns
- Canary rollout methodologies
- A/B testing for model validation
Monitoring and Observability in Production
- Key metrics for inference workloads
- Best practices for logging and tracing
- Creating dashboards and setting up alerts
Security and Reliability Best Practices
- Securing model endpoints
- Implementing network policies and access controls
- Ensuring high availability
Course Wrap-up and Future Directions
Requirements
- Proficiency in containerized application workflows
- Hands-on experience with Python-based machine learning models
- Working knowledge of Kubernetes fundamentals
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
