₹30 LPA MLOps Developer Cheat Sheet

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1. Foundations You Must Master

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Programming:

• Python (must-have)

  • Bash scripting

  • Basic Go

  • YAML (IaC, K8s, etc.)

• ML/DS Basics:

  • Supervised vs Unsupervised Learning

  • Feature Engineering

  • Model Evaluation Metrics

  • Building ML Pipelines

• Version Control:

  • Git

  • GitHub workflows

  • GitOps principles

• Linux & Shell:

  • Bash scripting

  • Filesystem commands

  • Crontab, Permissions, CLI tools

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2. Cloud Mastery

• Major Platforms:

  • Azure, AWS, GCP

• Key Concepts:

  • VMs, Object Storage, IAM, Networking

  • Compute options (VMs, Containers, Serverless)

• Cloud-Native Tools:

  • AKS, EKS, GKE

  • CI/CD pipelines using native tooling

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3. Containers + Orchestration

• Docker:

  • Build, tag, run, push images

  • Dockerfiles, Volumes, Networks

• Kubernetes:

  • Pods, Services, Deployments

  • ConfigMaps, Secrets, Helm Charts

• Kustomize:

  • Advanced config management for K8s deployments

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4. MLOps-Specific Tools

• Model Tracking:

  • MLflow, Weights & Biases, Neptune.ai

• Experimentation:

  • Jupyter, Papermill, TensorBoard

• Workflow Orchestration:

  • Kubeflow Pipelines, Airflow, Prefect

• Model Serving:

  • KServe, FastAPI, BentoML

• Monitoring:

  • Prometheus, Grafana, ELK Stack

• CI/CD:

  • GitHub Actions, GitLab CI, Jenkins, ArgoCD

• IaC:

  • Terraform, Pulumi, Ansible

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5. DevOps & Infra Skills (Non-Negotiable)

• CI/CD Pipelines:

  • Automate ML lifecycle: train, test, deploy

• Infrastructure as Code (IaC):

  • Terraform for reproducible infra

  • Pulumi/Ansible as secondary skills

• Monitoring & Logging:

  • Track system + model metrics

  • Detect model drift, failures

• Security:

  • Secrets management

  • IAM, RBAC controls

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6. Certifications That Matter

• Azure:

  • AZ-900 (Fundamentals)

  • AZ-104 (Admin)

  • DP-100 (ML Engineer)

• AWS:

  • SAA-C03 (Architect)

  • MLS-C01 (ML Specialty)

• GCP:

  • ACE (Associate Cloud Engineer)

  • Professional ML Engineer

• Others:

  • Terraform Associate

  • CKA (Certified Kubernetes Administrator)

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7. Mind-Blowing Portfolio Projects

• End-to-End ML Pipeline:

  • From data ingestion to deployment

• Kubeflow on Kubernetes:

  • DSL pipelines, KServe autoscaling

• MLflow + GitHub Actions:

  • CI/CD integrated model registry

• Monitoring Stack:

  • Prometheus + Grafana dashboards

• IaC Infra Deployment:

  • Terraform config for cloud infra

• Model Drift Detection + Retraining Pipelines

• Feature Store Integration:

  • Tecton / Feast + DVC

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8. Get Noticed: Resume, LinkedIn, GitHub

• Projects:

  • Deployed and documented E2E projects

• GitHub:

  • Clean structure, README, docker-compose examples

• LinkedIn:

  • Share progress, open-source work, problem-solving posts

• Resume:

  • Impact-driven, technical badge icons, quantifiable metrics

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9. Practice & Resources

• MLOps:

  • Made With ML

  • DeepLearning.AI MLOps specialization

• Kubernetes:

  • Kubernetes the Hard Way

  • Killer.sh

• Terraform:

  • HashiCorp Learn

  • YouTube series (e.g. FreeCodeCamp)

• CI/CD:

  • GitHub Actions Docs

  • ArgoCD Tutorials

• ML Basics:

  • Fast.ai, Andrew Ng’s ML + DL courses

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