Course Outline
Preparing Machine Learning Models for Production Deployment
- Packaging models using Docker
- Exporting models from TensorFlow and PyTorch
- Considering versioning and storage strategies
Serving Models on Kubernetes
- Introduction to inference server options
- Deploying TensorFlow Serving and TorchServe
- Configuring and managing model endpoints
Optimizing Inference Performance
- Implementing effective batching strategies
- Handling concurrent requests efficiently
- Tuning for optimal latency and throughput
Scaling ML Workloads
- Utilizing the Horizontal Pod Autoscaler (HPA)
- Using the Vertical Pod Autoscaler (VPA)
- Implementing Kubernetes Event-Driven Autoscaling (KEDA)
GPU Allocation and Resource Control
- Setting up GPU-enabled nodes
- Overview of the NVIDIA device plugin
- Defining resource requests and limits for ML workloads
Strategic Model Rollout and Release
- Applying Blue/green deployment patterns
- Executing canary rollout strategies
- Conducting A/B testing for model validation
Monitoring and Observability in Production
- Tracking key metrics for inference workloads
- Establishing best practices for logging and tracing
- Configuring dashboards and alerting systems
Security and Reliability Best Practices
- Securing model endpoints
- Implementing network policies and access control mechanisms
- Ensuring high availability for critical services
Summary and Future Directions
Requirements
- A solid grasp of containerized application workflows
- Practical experience with Python-based machine learning models
- Familiarity with fundamental Kubernetes concepts
Intended Audience
- ML engineers
- DevOps engineers
- Platform engineering teams
Testimonials (4)
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How trainer deliver knowledge so effectively
Vu Thoai Le - Reply Polska sp. z o. o.
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The knowledge and exchanges with Augustin