Self-Healing ML Systems on GCP: Automating Retraining and Rollbacks — Introduction

The Core Business Problem

Let’s face it: most machine learning models decay in silence.

They go stale.

Data drifts. Concepts shift. Customer behaviour evolves.
And your model? It's still serving predictions based on outdated assumptions.

The result?

• Lost revenue
• Lower accuracy
• No alerts, no retraining, no rollback

The Complete MLOps Vision: Self-Healing ML

A self-healing ML system is a production-grade solution that can:

• Detect degraded performance
• Automatically retrain using fresh data
• Evaluate improvements
• Deploy only if the new model is better
• Roll back if not
•  Log every step and alert your team

All of this, without human intervention.

The MLOps Stack (GCP Native)

Here’s what we’ll use to build it — all GCP-native tools:

Tool	Purpose
Vertex AI	Training, evaluation, and model deployment
Cloud Logging	Capture prediction logs
Pub/Sub	Trigger retraining on drift
Cloud Functions	Execute logic & kick off pipelines
BigQuery	Log metrics & system actions
Slack Webhooks (optional)	Notify your team in real-time

Architecture Overview

We’ll architect an event-driven, serverless MLOps system that looks like this:

           [Vertex AI Endpoint]

                   ↓

        [Cloud Logging: Model Scores]

                   ↓

     [Log-Based Alert → Pub/Sub Topic]

                   ↓

          [Cloud Function Trigger]

                   ↓

        [Vertex AI Training Pipeline]

                   ↓

     [Compare Eval Scores with Old Model]

                   ↓

     [Deploy if Better]    [Rollback if Worse]

                   ↓                  ↓

     [Notify via Slack]     [Log to BigQuery]

Key Features:

• Reactive: only retrain when drift is detected
• Autonomous: validates and deploys new models automatically
• Transparent: full audit trail with BigQuery logs
• Notifies: alerts your team with Slack, email, etc.

Why This is Unique

Most tutorials stop at deployment. This series picks up where they leave off. We’re building a production-grade, event-driven MLOps system with:

• Zero manual model promotion
•  Rollback if performance worsens
•  Real-time detection, no cron jobs
•  Fully serverless infrastructure
•  Zero third-party dependencies

What to Expect in This Series

This is Introduction Part — the series kickoff.

Here’s what’s coming next:

Part I: Monitoring & Triggers

1. Model Drift Detection with Vertex AI and Logging Alerts
2. Using Pub/Sub to Trigger Retraining
3. Cloud Functions as the Control Layer

Part II: Retraining Pipelines

4. Building Retraining Pipelines with Vertex AI Pipelines (KFP)
5. Model Evaluation & Auto-Deployment Logic
6. Using Cloud Scheduler & Firestore to Throttle Retraining

Part III: Observability & CI/CD

7. Slack Alerts and BigQuery Logging for MLOps Events
8. Visualizing Model Health with Looker or Grafana
9. CI/CD for ML Pipelines using GitHub Actions + Cloud Build

Part IV: Governance & Optimization

10. IAM, Cost Control, and GCP MLOps Best Practices
11. Feature Store + Explainable AI (Bonus)
12. Real-World Use Case: Demand Forecasting with Self-Healing Pipelines

What’s Up Next

In Part I, we’ll:

1. Model Drift Detection with Vertex AI and Logging Alerts
2. Using Pub/Sub to Trigger Retraining
3. Cloud Functions as the Control Layer

This will form the first trigger of our self-healing system.

Next Call to Action

• Bookmark this series
• Follow for Part 2