Week 4 Review: Kubernetes and Kubeflow Foundations
Review, Integrate, and Deploy a Complete Kubernetes-Based MLOps System with MLflow, FastAPI, HPA, and Kubeflow
By Sandip Das · · 30 min read
💡
Hey — It's Sandip Das 👋
Learn this to connect Kubernetes, Kubeflow, MLflow, FastAPI, and autoscaling concepts into one complete production-style ML system.
This practical review strengthens their ability to deploy, manage, scale, and troubleshoot end-to-end machine learning workloads confidently.
Learn this to connect Kubernetes, Kubeflow, MLflow, FastAPI, and autoscaling concepts into one complete production-style ML system.
This practical review strengthens their ability to deploy, manage, scale, and troubleshoot end-to-end machine learning workloads confidently.
📚 Key Learnings
- Why this matters
- Week 4 at a glance
- The four mental models to keep
- Rapid review, day by day
- A Quick Hands-on: The whole system on minikube
- Sketch the architecture
- Reading the CNCF landscape
- Troubleshooting Guide
🧠 Learn here
Six days of orchestration theory turn into one running system today. You will consolidate Days 22 to 27, sketch the architecture of a Kubernetes-based ML serving platform, and then actually build it on minikube: MLflow via Helm, a FastAPI model server with
Read the full article on learnxops.com.
Topics: 100 Days of MLOps, MLOps, ML, Kubernetes, Kubeflow