The EASIEST Way to Deploy ML Models on Kubernetes
Deploying Machine Learning models in production doesn’t have to be complicated. In this step-by-step beginner-friendly guide, I’ll show you how to train a model, turn it into an API using FastAPI, containerize it with Docker/Podman, and deploy it on Kubernetes like any other microservice.
You’ll learn exactly how ML systems are deployed in the real world:
• Model training with scikit-learn
• Saving a model as model.pkl
• Building a FastAPI inference API
• Creating a lightweight container image
• Deploying it to Kubernetes using Deployment, Service, and Ingress
• Testing predictions live from the cluster
Whether you're new to Kubernetes, exploring MLOps, or want to understand how companies deploy ML models at scale, this tutorial walks you through everything end-to-end.
This video is perfect for beginners and DevOps engineers who want to get hands-on with ML model serving.
If you want more ML deployment, MLOps, Kubernetes, HPA/VPA scaling, or DevOps content, hit subscribe because the next videos go deeper into autoscaling ML pods and real production setups.
▬▬▬▬▬▬▬ Timestamps ▬▬▬▬▬▬▬
00:00 - Intro
00:27 - What Is Model Training?
00:53 - What Is Inference?
01:23 - Installing Python Dependencies
02:33 - Training the ML Model
03:29 - Building the FastAPI Inference Server
06:36 - Containerizing the ML App (Docker/Podman)
09:59 - Deploying the ML Model on Kubernetes
15:00 - Recap and Next Steps
For 1:1 mentorship, consultations, or career guidance, you can book a session with me here: https://topmate.io/nikhil_kumar811/
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