Course Outline
Preparing Machine Learning Models for Deployment
- Encapsulating models using Docker
- Exporting models from TensorFlow and PyTorch
- Best practices for versioning and storage
Serving Models on Kubernetes
- Introduction to inference servers
- Deployment of TensorFlow Serving and TorchServe
- Configuration of model endpoints
Optimizing Inference Performance
- Implementing batching strategies
- Managing concurrent requests effectively
- Tuning for optimal latency and throughput
Autoscaling Machine Learning Workloads
- Horizontal Pod Autoscaler (HPA)
- Vertical Pod Autoscaler (VPA)
- Kubernetes Event-Driven Autoscaling (KEDA)
GPU Allocation and Resource Management
- Setting up GPU-enabled nodes
- Overview of the NVIDIA device plugin
- Defining resource requests and limits for ML workloads
Model Rollout and Release Methodologies
- Blue/green deployment patterns
- Canary rollout techniques
- A/B testing for rigorous model evaluation
Monitoring and Observability for Production ML
- Key metrics for inference workloads
- Standard logging and tracing procedures
- Creation of dashboards and alerting systems
Security and Reliability Enhancements
- Protecting model endpoints
- Implementing network policies and access controls
- Maintaining high availability
Summary and Future Directions
Requirements
- A solid grasp of containerized application workflows
- Practical experience with Python-based machine learning models
- Working knowledge of Kubernetes fundamentals
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