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Duration 14 hours
Course Outline
Preparing Machine Learning Models for Deployment
- Encapsulating 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
- Configuring 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 Allocation and Resource Management
- Setting up GPU nodes
- Overview of the NVIDIA device plugin
- Defining resource requests and limits for ML tasks
Model Rollout and Release Management
- Blue/green deployment methods
- Canary release patterns
- Utilizing A/B testing for model validation
Monitoring and Observability for Production ML
- Key metrics for inference workloads
- Best practices for logging and tracing
- Setting up dashboards and alerts
Security and Reliability Measures
- Protecting model endpoints
- Implementing network policies and access controls
- Guaranteeing high availability
Summary and Next Steps
Requirements
- Knowledge of containerized application workflows
- Hands-on experience with Python-based machine learning models
- Basic familiarity with 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
