Course Outline
Preparing Machine Learning Models for Deployment
- Packaging 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
- Establishing model endpoints
Techniques for Optimizing Inference
- Strategies for batching
- Handling concurrent requests
- Tuning for latency and throughput
Autoscaling ML Workloads
- Horizontal Pod Autoscaler (HPA)
- Vertical Pod Autoscaler (VPA)
- Kubernetes Event-Driven Autoscaling (KEDA)
Provisioning GPUs and Managing Resources
- Configuration of GPU nodes
- Overview of the NVIDIA device plugin
- Defining 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
- Best practices for logging and tracing
- Dashboards and alerting systems
Security and Reliability Considerations
- Securing model endpoints
- Network policies and access control
- Ensuring high availability
Summary and Next Steps
Requirements
- A solid grasp of containerized application workflows
- Practical experience with Python-based machine learning models
- Basic familiarity with Kubernetes concepts
Target Audience
- ML engineers
- DevOps engineers
- Platform engineering teams
Testimonials (4)
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How trainer deliver knowledge so effectively
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The knowledge and exchanges with Augustin