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MLOps beginner Lesson 1 of 8

MLOps Learning Roadmap

A structured path from experiment tracking basics to building fully automated ML platforms — covering CI/CD, monitoring, data versioning, and model serving.

Why MLOps

A model that works in a notebook has zero business value. MLOps is the discipline that takes models from research to production: reproducible training, automated pipelines, reliable deployment, and continuous monitoring. It’s what separates a prototype from a system.

Stage 1: Experiment Tracking and Reproducibility (Week 1)

Goal: Make experiments reproducible and comparable.

Topics:

Milestone: Take any existing ML script and add full MLflow tracking: params, per-epoch metrics, final scores, model artifact, and git commit hash. Run 10 experiments and identify the best configuration from the MLflow UI.

Stage 2: Data Versioning (Week 2)

Goal: Version datasets and pipelines alongside code.

Topics:

Milestone: Build a DVC pipeline with 3 stages (preprocess, train, evaluate). Use params.yaml for all hyperparameters. Run 5 experiments with dvc exp run and use dvc metrics diff to produce a comparison report.

Stage 3: Model Serving (Week 3)

Goal: Deploy models as production-grade REST APIs.

Topics:

  • Model Serving — FastAPI server, batch inference, blue-green deployment

Milestone: Deploy a trained model as a FastAPI service: model loads on startup, /predict endpoint with input validation, /health endpoint, and a /metrics endpoint showing request count and latency.

Stage 4: CI/CD for ML (Week 4)

Goal: Automate training, testing, and deployment.

Topics:

  • CI/CD Pipelines — GitHub Actions for ML, automated testing, Docker builds, deployment gates

Milestone: Set up a GitHub Actions workflow that: trains a model on a data sample, evaluates it against a baseline, fails the PR if metrics regress, and builds + pushes a Docker image if checks pass.

Stage 5: Monitoring and Drift (Week 5)

Goal: Detect and respond to model degradation in production.

Topics:

Milestone: Build a monitoring pipeline that: logs all predictions with input features, computes daily KS test and PSI scores against training distribution, and sends an alert when drift exceeds thresholds.

Stage 6: Projects

Beginner: Dockerized model server, automated retraining trigger, environment reproducibility
Intermediate: Full MLflow pipeline, DVC pipeline with experiments, CI/CD for ML
Advanced: Kubeflow pipelines, real-time feature store, LLMOps platform

See MLOps Projects for full descriptions.

What Comes After MLOps

  • Kubeflow / Vertex AI Pipelines — ML pipelines at enterprise scale
  • Feature stores — Feast, Tecton for centralized feature management
  • Model monitoring — Evidently AI, Arize, Fiddler for production monitoring
  • LLMOps — specialized tooling for LLM cost tracking, prompt versioning, evaluation

Resources

  • MLflow documentation: mlflow.org/docs
  • DVC documentation: dvc.org/doc
  • “Designing Machine Learning Systems” by Chip Huyen — essential reading
  • Made With ML: madewithml.com — applied MLOps tutorials
  • Full Stack Deep Learning course: fullstackdeeplearning.com

Frequently Asked Questions

Do I need to be an ML engineer to learn MLOps?
You need ML fundamentals (training models, evaluating them, understanding overfitting) and software engineering basics (Git, Python, Docker, REST APIs). MLOps is the bridge between ML and software engineering — it's the set of practices that makes ML reproducible, automated, and maintainable in production.
What's the most impactful MLOps skill to learn first?
Experiment tracking with MLflow. It costs almost nothing to add to an existing workflow, immediately makes your experiments reproducible, and gives you a foundation for model registry and deployment. Most teams that skip experiment tracking end up unable to reproduce their best models — a painful lesson.