⚡CI/CD for AI — Auto Retrain, Auto Test, Auto Reject, Auto Promote (EP5)

MLOps Series — How Real Companies Protect Production Models

Author

Arbaz M
November 29, 2025

🎯 Why This Episode Matters

In software engineering, CI/CD catches bugs instantly.
In machine learning, a model can get worse after retraining — and everything will still look “healthy.”

No crashes.
No errors.
No logs.

Just silently deteriorating predictions.

This is one of the biggest risks in production ML systems.

Episode 5 introduces the solution companies actually use:

CI/CD for AI.

A pipeline that:

  • retrains models automatically

  • evaluates them against the current production model

  • rejects bad models

  • promotes only the better model

  • updates production safely

This is the layer that keeps ML from breaking silently.

📌 What We Build in Episode 5

Our folder now looks like this: 

artifacts_prod/         # Production model
    pipeline.pkl      

artifacts_candidate/    # New model for testing
    pipeline.pkl      

model/
    train_good_model.py
    train_bad_model.py
    evaluate_pipeline.py
    promote.py
    api_v2.py

.github/workflows/
    mlops-cicd.yaml

This setup mirrors how real companies govern ML systems.

We will:

  • train a GOOD production model

  • train a BAD candidate model

  • compare both on the same dataset

  • reject the candidate

  • promote only the better one

  • automate everything with GitHub Actions

By the end of the episode, you’ll have a fully working model governance + CI/CD pipeline.

🟢 Training the GOOD Production Model

We start by training a reliable, high-performing baseline model and saving it into: 

artifacts_prod/pipeline.pkl

This represents the “trusted” model currently in production.

This is the version our API is serving — and the version all future candidates must beat.

🔴 Training the BAD Candidate Model

Next, we intentionally create a bad model.

We simulate what actually happens in real teams:

  • bad training data

  • noisy labels

  • truncated vocabulary

  • wrong preprocessing

  • reduced dataset

The model looks normal, trains without errors, and outputs predictions…

…but its accuracy is MUCH worse.

This is exactly how bad models slip through without proper safeguards.

⚖️ Evaluating: Good vs Bad

Our evaluation script loads both models, tests them against the same dataset, and prints: 

Production model accuracy: 0.xx
Candidate model accuracy: 0.xx
Reject new model

If the candidate model is worse, we reject it.

If it’s better, we promote it.

This is the core logic behind CI/CD for ML.

🟢 Promotion Logic — Only When Better

If the candidate passes the quality gate, a simple script replaces the production pipeline: 

artifacts_candidate/pipeline.pkl
→ artifacts_prod/pipeline.pkl

This ensures our FastAPI service will automatically serve the updated model without any code changes.

Production always loads: 

artifacts_prod/pipeline.pkl

Stable. Safe. Predictable.

🚀 GitHub Actions CI/CD for MLOps

Episode 5 introduces a fully automated CI/CD workflow:

  • installs Python

  • installs dependencies

  • trains candidate model

  • evaluates candidate vs production

  • auto-rejects bad candidates

  • auto-promotes better ones

  • commits the updated production model

Every push to main triggers the full loop.

This is EXACTLY how real ML teams prevent silent failures.

🧠 What EP5 Teaches You

The key idea:

In ML, new is not always better.

A retrained model can hurt performance, even if code and infrastructure look perfect.

Episode 5 teaches:

  • model governance

  • quality gates

  • automated evaluation

  • regression control

  • safe promotion

  • how CI/CD works for ML

  • how modern companies avoid silent model degradation

This is one of the most important real-world MLOps skills.

🚀 Coming Up in Episode 6

Episode 6 brings the next critical piece:

  • monitoring

  • drift detection

  • Prometheus / Grafana

  • alerts

  • real-time tracking of inputs + outputs

CI/CD protects deployments.
Monitoring protects everything after deployment.

🔗 Full Video + Code Access

🎥 Watch Episode 5: https://youtu.be/DlNzxFMXLic

Subscribers get:

  • complete CI/CD pipeline

  • production-ready workflow

  • real incident labs

  • drift simulation scripts

  • MLflow examples

  • interview prep

  • all episode code bundles

💼 Need DevOps or MLOps Help?

If you’re building:

  • CI/CD pipelines

  • Docker + Jenkins

  • MLflow setups

  • FastAPI deployments

  • model governance workflows

  • monitoring + alerting

  • Kubernetes

  • cloud cost optimization

You can consult me directly.

Reply to this email or message me on YouTube/Instagram.