MLOps Fundamentals Cheat Sheet

Summarizes MLOps practices for experiment tracking, model versioning, and continuous training pipelines using tools like MLflow, DVC, and CI/CD automation.

2 PagesIntermediateFeb 28, 2026

Experiment Tracking with MLflow

Log parameters, metrics, and models for every training run.

python

import mlflowimport mlflow.sklearnmlflow.set_experiment("churn-model")with mlflow.start_run():    model.fit(X_train, y_train)    acc = model.score(X_test, y_test)    mlflow.log_param("n_estimators", 100)    mlflow.log_metric("accuracy", acc)    mlflow.sklearn.log_model(model, "model")# View the UI:  mlflow ui --port 5000

CI Pipeline for Retraining

Automate scheduled model retraining with GitHub Actions.

yaml

name: retrain-modelon:  schedule:    - cron: "0 3 * * 1"   # every Monday 3am  workflow_dispatch: {}jobs:  train:    runs-on: ubuntu-latest    steps:      - uses: actions/checkout@v4      - uses: actions/setup-python@v5        with:          python-version: "3.11"      - run: pip install -r requirements.txt      - run: python train.py      - run: python evaluate.py --min-accuracy 0.85

Core Concepts

Foundational ideas behind an MLOps workflow.

MLOps- Practices that apply DevOps principles (CI/CD, automation, monitoring) to the machine learning lifecycle
Feature store- Central repository for versioned, reusable features shared between training and serving
Model registry- Versioned catalog of trained models with lifecycle stages (staging, production, archived)
Reproducibility- Ability to recreate a model exactly via pinned data, code, and dependency versions
Data versioning- Tracking dataset versions (e.g. with DVC) alongside code so experiments are traceable
Continuous training (CT)- Automatically retraining models on new data on a schedule or trigger

Common Tooling

Widely used tools across the MLOps stack.

MLflow- Open-source platform for experiment tracking, model packaging, and a model registry
DVC- Data Version Control; git-like versioning for datasets and ML pipelines
Kubeflow- Kubernetes-native platform for orchestrating ML pipelines
Airflow- Workflow orchestrator commonly used to schedule ETL and training DAGs
Weights & Biases- Experiment tracking and visualization tool similar to MLflow

Pro Tip

Treat your training pipeline as code: pin dependency versions, seed random states, and version the training data - otherwise 'reproduce this model' becomes impossible six months later.

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