model-deployment

Purpose

This skill automates the deployment of machine learning models to production environments using containers (e.g., Docker) and orchestration tools (e.g., Kubernetes), ensuring scalable and reliable ML model serving.

When to Use

• When you need to containerize and deploy a trained ML model for real-time inference in production.
• For updating existing deployments in response to model retraining or performance issues.
• In MLOps pipelines where models must be versioned, monitored, and rolled back easily.
• When integrating with cloud providers like AWS EKS or Google GKE for managed orchestration.

Key Capabilities

• Builds Docker images from model artifacts and deploys them to Kubernetes clusters.
• Supports model versioning via tags and handles rolling updates for zero-downtime deployments.
• Integrates with ML frameworks like TensorFlow or PyTorch for serving models via APIs.
• Manages resource allocation, such as CPU/GPU requests in Kubernetes pods, e.g., resources: limits: cpu: 2.
• Automates scaling based on traffic, using Kubernetes Horizontal Pod Autoscalers.