Lilly Tech Systems
Model Serving on Kubernetes
Deploy and manage ML models at production scale on Kubernetes. Compare and master the leading serving frameworks — NVIDIA Triton, KServe, Seldon Core, and BentoML — for reliable, scalable inference.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Understand model serving concepts, inference patterns, and the K8s serving ecosystem landscape.
2. NVIDIA Triton
Deploy models with Triton Inference Server for high-performance multi-framework serving with dynamic batching.
3. KServe
Use KServe for serverless model serving with autoscaling, canary deployments, and model transformers.
4. Seldon Core
Build advanced inference graphs with Seldon Core for A/B testing, multi-armed bandits, and model explanations.
5. BentoML
Package and deploy models with BentoML for developer-friendly containerized serving with adaptive batching.
6. Best Practices
Production patterns for model versioning, monitoring, A/B testing, latency optimization, and cost management.
What You'll Learn
By the end of this course, you'll be able to:
Deploy Any Model
Serve PyTorch, TensorFlow, ONNX, and custom models using the right framework for your requirements.
Optimize Performance
Configure dynamic batching, model ensembles, and GPU acceleration for low-latency inference.
Scale Automatically
Set up autoscaling based on request volume, GPU utilization, or custom metrics.
Production Operations
Implement canary deployments, monitoring, alerting, and rollback strategies for model serving.