What is MLOps? ML Engineering Best Practices

In this tutorial, you'll learn about What is MLOps? ML Engineering Best Practices. We cover key concepts, practical examples, and best practices to help you understand and apply this topic effectively.

What You'll Learn

Understand MLOps — the practices and tools for managing Machine Learning models from development through production, monitoring, and iteration.

Why It Matters

Most ML projects fail not because the model is bad, but because the surrounding infrastructure is broken. MLOps fixes that.

Real-World Use

Managing hundreds of models at a company like Netflix or Uber, ensuring regulatory Compliance for ML in healthcare/finance, and preventing model drift from breaking production systems.

What is MLOps?

MLOps (Machine Learning Operations) applies DevOps principles to ML:

• Version control for data, code, and models
• Automated training and deployment
• Monitoring for model degradation
• Reproducible experiments

The ML Lifecycle

Data → Training → Evaluation → Deployment → Monitoring
  ↑                                            |
  └──────────────── Iterate ────────────────────┘

Key MLOps Practices

Experiment Tracking

Log every training run so you can compare and reproduce:

import mlflow

mlflow.set_experiment("sentiment-analysis")

with mlflow.start_run():
    mlflow.log_param("learning_rate", 0.001)
    mlflow.log_param("epochs", 10)
    mlflow.log_metric("accuracy", 0.97)
    mlflow.log_artifact("model.pkl")

Data Versioning

Track which dataset version trained each model:

dvc add data/training_set.csv
git add data/training_set.csv.dvc
git commit -m "Add training dataset v3"

Model Registry

Store models with metadata for production deployment:

# MLflow model registry
mlflow models serve -m "models:/sentiment-model/Production"

CI/CD for ML

Test and validate before deploying:

# .github/workflows/ml-pipeline.yml
name: ML Pipeline
on: [push]
jobs:
  train:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - run: pip install -r requirements.txt
      - run: python train.py
      - run: python evaluate.py  # Fail if accuracy drops

Model Monitoring