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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
14 Hours
Testimonials (3)
Good and feasible exercises.
Jannes Wykhoff - Landesamt fur Geoinformation und Landesvermessung Niedersachsen (LGLN)
Course - Certified Kubernetes Application Developer (CKAD) - exam preparation
Machine Translated
About the microservices and how to maintenance kubernetes
Yufri Isnaini Rochmat Maulana - Bank Indonesia
Course - Advanced Platform Engineering: Scaling with Microservices and Kubernetes
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