30 Days of MLOps

Master MLOps in 30 days — from ML pipelines to production deployment, model monitoring, and automation.

30 of 30 days published.

The path

  1. Intro to MLOps – ML Meets DevOps 5 min read
  2. MLOps Tools Landscape – Explore the Ecosystem 6 min read
  3. Data Versioning with DVC – Reproducible ML Starts with Data 5 min read
  4. Reproducible ML environments using Conda & Docker 5 min read
  5. Feature Engineering & Feature Stores – Fueling ML with Quality Features 9 min read
  6. Training ML Models with Scikit-learn & TensorFlow – Build & Save Your Models Like a Pro 11 min read
  7. Model Experiment Tracking with MLflow – Log It or Lose It 5 min read
  8. Model Evaluation & Metrics – Measure What Matters 8 min read
  9. ML Pipelines with Kubeflow Pipelines - Automate & Orchestrate ML Workflows 11 min read
  10. Serving ML Models with FastAPI & Flask 6 min read
  11. Packaging Models with Docker – Containerize & Deploy Your ML Models 6 min read
  12. CI/CD for ML with GitHub Actions – Automate Test-Train-Deploy Pipelines 7 min read
  13. ML Model Deployment – Batch vs Real-time Inference 5 min read
  14. Data Drift & ML Model Drift Detection – Keep Your Models Relevant 4 min read
  15. Automated Retraining ML Pipelines To Keep Your ML Models Fresh 6 min read
  16. Security in MLOps – Protecting ML Systems at Every Layer 9 min read
  17. ML Model Governance & Compliance – Auditing, Explainability & Fairness in ML 7 min read
  18. Explainable AI (XAI) in Production – SHAP, LIME, and Interpretability Techniques 8 min read
  19. Monitoring ML Systems in Production – Metrics, Logging, Alerting 7 min read
  20. Model Registry – Managing and Versioning ML Models 5 min read
  21. Scaling ML Model Inference with Kubernetes 8 min read
  22. MLOps with ML Platforms (SageMaker & Vertex AI) 8 min read
  23. Managing Large Language Models (LLMs) in Production 13 min read
  24. Agentic AI & Retrieval‑Augmented Generation (RAG) 9 min read
  25. Model Context Protocol (MCP) Explained for MLOps Engineers 8 min read
  26. Project: End-to-End MLOps Pipeline 10 min read
  27. Model Deployment with Serverless Architectures 20 min read
  28. Cost Optimization & Performance Tuning in MLOps 8 min read
  29. Disaster Recovery & High Availability for ML Systems 9 min read
  30. MLOps Interview Question & Answers 21 min read