Course Outline
Preparing Machine Learning Models for Deployment
- Packaging models with Docker
- Exporting models from TensorFlow and PyTorch
- Versioning and storage considerations
Model Serving on Kubernetes
- Overview of inference servers
- Deploying TensorFlow Serving and TorchServe
- Setting up model endpoints
Inference Optimization Techniques
- Batching strategies
- Concurrent request handling
- Latency and throughput tuning
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
- An understanding of containerized application workflows
- Experience with Python-based machine learning models
- Familiarity with Kubernetes fundamentals
Audience
- ML engineers
- DevOps engineers
- Platform engineering teams
Testimonials (5)
Interactivity, no reading slides all day
Emilien Bavay - IRIS SA
Course - Kubernetes Advanced
Practical examples, the possibility of independently testing the discussed topics.
Kamil - Volkswagen Poznan Sp. z o.o.
Course - Docker, Kubernetes and OpenShift 3 for Administrators
Machine Translated
he was patience and understood that we fall behind
Albertina - REGNOLOGY ROMANIA S.R.L.
Course - Deploying Kubernetes Applications with Helm
Interactive way of conducting training.
Krzysztof Kupisz - Kredyt Inkaso S.A. Centrum Operacyjne w Lublinie
Course - Managing Kubernetes with Rancher
Machine Translated
The training was more practical