Gourav Shah initcron

Setting up Argo Locally on existing Kubernetes Cluster

Create Argo Namespace

kubectl create namespace argo
kubectl get ns

Setup Argo Version and Install it

Does an AI Platform Engineer Need Data Science Essentials?

Short Answer: Yes, but only selectively.

As an AI Platform Engineer, the focus is on building, deploying, and optimizing AI/ML models at scale, not on developing new ML algorithms or performing deep data science research. However, to work effectively with Data Scientists and MLOps workflows, an AI Platform Engineer should understand key Data Science essentials related to:

✅ Understanding ML model workflows (How data moves through AI/ML pipelines)
✅ Feature Engineering & Feature Stores (How data is prepped for models)
✅ Fine-tuning & Inference Optimization (How models are trained and served efficiently)
✅ Evaluating Model Performance (Ensuring models meet production-quality standards)

Mini Project Spec: Host Your Resume on AWS S3

Introduction:

In this mini-project, you will learn how to use Amazon S3 to host your resume, creating a publicly accessible web-based version of your professional profile. This is a practical skill that can be applied in real-world scenarios, such as job applications or networking events.

Project Goal:

To host an HTML version of your resume on AWS S3.

	apiVersion: apps/v1
	kind: Deployment
	metadata:
	name: vote
	labels:
	role: vote
	annotations:
	kubernetes.io/change-cause: "image updated to v4"
	spec:
	replicas: 4

	{
	"mcpServers": {
	"kubernetes": {
	"command": "npx",
	"args": [
	"-y",
	"kubernetes-mcp-server@latest"
	]
	},

	apiVersion: argoproj.io/v1alpha1
	kind: ApplicationSet
	metadata:
	name: instavote
	namespace: argocd
	spec:
	generators:
	- matrix:
	generators:
	- git:

	# This is an auto-generated file. DO NOT EDIT
	apiVersion: v1
	kind: ServiceAccount
	metadata:
	namespace: argo-rollouts
	labels:
	app.kubernetes.io/component: argo-rollouts-dashboard
	app.kubernetes.io/name: argo-rollouts-dashboard
	app.kubernetes.io/part-of: argo-rollouts
	name: argo-rollouts-dashboard

	apiVersion: argoproj.io/v1alpha1
	kind: ApplicationSet
	metadata:
	name: instavote-dev
	namespace: argocd
	spec:
	generators:
	- git:
	repoURL: https://github.com/initcron/instavote-gitops
	revision: HEAD

	apiVersion: argoproj.io/v1alpha1
	kind: Workflow
	metadata:
	generateName: vote-ci-
	spec:
	entrypoint: main
	arguments:
	parameters:
	- name: repo-url
	value: "https://github.com/xxxxxx/vote.git"