Create Argo Namespace
kubectl create namespace argo
kubectl get ns
Setup Argo Version and Install it
# .github/workflows/mlops-pipeline.yml | |
name: MLOps Pipeline | |
on: | |
workflow_dispatch: | |
inputs: | |
run_all: | |
description: 'Run all jobs' | |
required: false | |
default: 'true' |
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: ApplicationSet | |
metadata: | |
name: instavote | |
namespace: argocd | |
spec: | |
generators: | |
- matrix: | |
generators: | |
- git: |
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" |
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)