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Cloud, DevOps, and MLOps are critical for modern IT infrastructure, enabling automation, scalability, and efficient machine learning workflows. Below are key commands, tools, and practices to master these domains.
You Should Know:
1. Essential Cloud & DevOps Commands
AWS CLI (Amazon Web Services):
aws configure Set up AWS credentials aws s3 ls List S3 buckets aws ec2 describe-instances Check EC2 instances
Terraform (Infrastructure as Code):
terraform init Initialize Terraform terraform plan Preview changes terraform apply Apply infrastructure changes
Kubernetes (Container Orchestration):
kubectl get pods List running pods kubectl apply -f deployment.yaml Deploy a Kubernetes manifest kubectl logs <pod-name> Check pod logs
2. MLOps & Machine Learning Workflow
MLflow (Experiment Tracking):
mlflow ui Launch MLflow tracking server mlflow run . -P alpha=0.5 Run an ML project
Docker (Containerization for ML Models):
docker build -t ml-model . Build a Docker image docker run -p 5000:5000 ml-model Run the model API
CI/CD Pipeline (GitHub Actions Example):
name: CI/CD Pipeline on: [bash] jobs: build: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - run: pip install -r requirements.txt - run: pytest
3. Linux & Windows Admin Commands
Linux (Security & Monitoring):
sudo apt update && sudo apt upgrade -y Update system journalctl -u docker Check Docker logs netstat -tuln List open ports
Windows (PowerShell for DevOps):
Get-Service | Where-Object Status -eq "Running" List running services Test-NetConnection -ComputerName google.com -Port 443 Check network connectivity
What Undercode Say:
Mastering Cloud, DevOps, and MLOps requires hands-on practice with automation, containerization, and CI/CD pipelines. The future of IT lies in scalable, AI-driven infrastructure, making these skills indispensable.
Prediction:
By 2025, 80% of enterprises will adopt AI-powered DevOps, with MLOps becoming a standard practice for deploying machine learning models efficiently.
Expected Output:
- AWS, Kubernetes, and Terraform commands executed successfully.
- MLflow tracking server running on `http://localhost:5000`.
- Docker container deployed with ML model API.
- CI/CD pipeline triggered on Git push.
Relevant URLs:
IT/Security Reporter URL:
Reported By: Sandip Das – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
