Desktop only.

This device is not compatible.

Home>
Projects>

Deploy and Scale an AI Inference Service on Kubernetes

PROJECT

Deploy and Scale an AI Inference Service on Kubernetes

In this project, we’ll build a terminal-based workflow to deploy, operate, and autoscale an AI service on Kubernetes using Docker, kind, and kubectl. We’ll create an optimized NGINX image, provision a local cluster, configure the application with Kubernetes manifests, define resource requests and limits, stream logs, and inspect events to troubleshoot deployment and scheduling issues.

Deploy and Scale an AI Inference Service on Kubernetes

You will learn to:

Containerize an application using a Dockerfile.

Provision a local Kubernetes cluster using kind and side-load container images.

Create Kubernetes Deployments, Services, ConfigMaps, and Secrets.

Configure Liveness and Readiness probes to enable automated self-healing.

Skills

Docker Containers

Docker

Kubernetes

Prerequisites

Basic knowledge of Docker and containerization

Familiarity with Kubernetes concepts (Pods, Deployments, Services)

Understanding of YAML syntax and declarative configuration

Comfort with the Linux command line and basic networking

Basic knowledge of React builds and NGINX

Technologies

Docker

Node.js

Project Description

Deploying modern applications consistently across environments requires containerization and orchestration. Docker packages applications into portable container images, while Kubernetes manages workload deployment, health checks, and resource allocation.

In this project, we’ll build an end-to-end containerization and orchestration workflow for an AI Meeting Notes frontend application. We’ll use a multi-stage Dockerfile to build the static React application files and serve them with NGINX. We’ll then create a local Kubernetes cluster with kind and deploy the application using declarative manifests for a Deployment, Service, and ConfigMap.

We’ll configure port mappings to access the application, define container resource requests and limits, and troubleshoot scheduling failures. We’ll also configure Liveness and Readiness probes, stream application logs, and inspect Kubernetes events. By the end of the project, we’ll have practical experience with Docker containerization, Kubernetes resource management, application health monitoring, troubleshooting, and observability.

Project Tasks

1

Introduction

Task 0: Get Started

Task 1: Set Up Local Application

2

Containerization and Cluster Setup

Task 2: Configure Dockerfile and Build Image

Task 3: Configure kind-config and Cluster

3

Network Exposure

Task 4: Configure ConfigMap and Secret

Task 5: Configure Deployment and Service Manifest

Task 6: Deploy and Verify Workloads

4

Resource Debugging and Health Probes

Task 7: Simulate Failures

Task 8: Add Health Probes

5

Operational Monitoring and Inspection

Task 9: Monitor Application Logs and Cluster Events

6

Wrap Up

Congratulations!

has successfully completed the Guided ProjectDeploy and Scale an AI Inference Serviceon Kubernetes

Subscribe to project updates

Hear what others have to say
Join 1.4 million developers working at companies like
"Another great hands on project to apply your knowledge learned. Thank you Educative ❤️"

Atabek BEKENOV

Senior Software Engineer

"Super excited to learn E-commerce website for my own startup venture. Thanks for your great learning platform."

Pradip Pariyar

Senior Software Engineer

"This was an excellent lesson. I learned a lot working through the process. I enjoyed it so much that I rebuilt it my AWS account to see how hard it would be to deploy to a production environment."

Renzo Scriber

Senior Software Engineer

"It was my first proper data engineering project and it was amazing."

Vasiliki Nikolaidi

Senior Software Engineer

"It's a fantastic way to do hands-on practice; I enjoy this way of learning."

Juan Carlos Valerio Arrieta

Senior Software Engineer

Relevant Courses

Use the following content to review prerequisites or explore specific concepts in detail.