Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Preparing Machine Learning Models for Deployment
- Packaging models with Docker.
- Exporting models from TensorFlow and PyTorch.
- Considerations for versioning and storage.
Model Serving on Kubernetes
- Overview of inference servers.
- Deploying TensorFlow Serving and TorchServe.
- Setting up model endpoints.
Inference Optimization Techniques
- Batching strategies.
- Handling concurrent requests.
- Tuning for latency and throughput.
Autoscaling ML Workloads
- Horizontal Pod Autoscaler (HPA).
- Vertical Pod Autoscaler (VPA).
- Kubernetes Event-Driven Autoscaling (KEDA).
GPU Provisioning and Resource Management
- Configuring GPU nodes.
- Overview of the NVIDIA device plugin.
- Setting 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
- A solid understanding of containerized application workflows.
- Hands-on experience with Python-based machine learning models.
- Familiarity with the fundamentals of Kubernetes.
Audience
- ML engineers.
- DevOps engineers.
- Platform engineering teams.
14 Hours
Testimonials (3)
About the microservices and how to maintenance kubernetes
Yufri Isnaini Rochmat Maulana - Bank Indonesia
Course - Advanced Platform Engineering: Scaling with Microservices and Kubernetes
How trainer deliver knowledge so effectively
Vu Thoai Le - Reply Polska sp. z o. o.
Course - Certified Kubernetes Administrator (CKA) - exam preparation
The knowledge and the patience from the trainer to answer to our questions.
