MLOps

Introduction

MLOps extends DevOps practices to models: versioned data, reproducible training, staged deployment, monitoring drift, and retraining triggers.

Understanding the topic

Artifacts Track datasets, code commit, metrics, and serialized model together.

Serving Expose predictions via batch jobs or online APIs with health checks.

Monitoring Watch latency, error rate, and input distribution shift.

• Artifacts — Track datasets, code commit, metrics, and serialized model together.
• Serving — Expose predictions via batch jobs or online APIs with health checks.
• Monitoring — Watch latency, error rate, and input distribution shift.

Step-by-step explanation

1. Artifacts — Track datasets, code commit, metrics, and serialized model together.
2. Serving — Expose predictions via batch jobs or online APIs with health checks.
3. Monitoring — Watch latency, error rate, and input distribution shift.

Execution workflow

Best practices

• Hold out a test set before hyperparameter tuning.
• Scale numeric columns for distance-based models.
• Track multiple metrics — not accuracy alone on skewed labels.

Common mistakes

• Leaking test statistics into preprocessing fit on full data.
• Training on the same rows you report as test performance.
• Chasing complex models before a simple baseline.

Hands-on exercise

Practice:

• Sketch a CI job that trains and registers a model
• List three production alerts you would add

Summary

MLOps — Operational lifecycle for production models.