MLOps on Vertex AI: Best Practices

After implementing multiple MLOps pipelines on Vertex AI for enterprise clients, I've learned that success comes down to a few key principles. Here's what actually works in production.

Why Vertex AI for MLOps?

Vertex AI provides a unified platform for the entire ML lifecycle, but more importantly, it integrates seamlessly with the GCP ecosystem. If you're already on Google Cloud, Vertex AI eliminates the integration headaches you'd face with standalone MLOps tools.

Key Advantages

• Unified Platform - Training, deployment, monitoring in one place
• Managed Infrastructure - No need to maintain Kubeflow or custom orchestration
• Enterprise Features - VPC-SC, customer-managed encryption, audit logging
• AutoML Options - Quick baselines when appropriate

Pipeline Architecture Patterns

I typically structure Vertex AI pipelines using three distinct patterns, depending on the use case:

1. Continuous Training Pipeline

For models that need frequent retraining with new data:

• Scheduled runs (daily, weekly)
• Data validation checks before training
• Automated model evaluation against current production model
• Conditional deployment based on performance metrics

2. On-Demand Training Pipeline

For models that retrain based on triggers rather than schedule:

• Event-driven (new dataset available, data drift detected)
• Manual trigger option for data scientists
• More complex validation logic

3. Experimentation Pipeline

For rapid iteration during model development:

• Lightweight components for quick iteration
• Experiment tracking with Vertex AI Experiments
• Easy parameter sweeps and hyperparameter tuning

Model Registry Best Practices

The Vertex AI Model Registry is more than just a storage location. Use it strategically:

• Versioning Strategy - Use semantic versioning with meaningful tags
• Metadata - Store training metrics, dataset versions, and hyperparameters
• Staging Labels - Mark models as dev, staging, production
• Model Cards - Document model purpose, limitations, and bias considerations

Deployment Strategies That Work

Vertex AI supports multiple deployment patterns. Here's when to use each:

Online Prediction (Real-time)

Best for:

• Low-latency requirements (< 100ms)
• User-facing applications
• Request/response patterns

Use autoscaling, but set min instances > 0 for production to avoid cold starts.

Batch Prediction

Best for:

• Large-scale inference jobs
• Non-time-sensitive predictions
• Cost optimization (runs on preemptible instances)

Traffic Splitting for A/B Testing

Vertex AI makes it easy to split traffic between model versions:

• Start with 5-10% to new model
• Monitor metrics closely
• Gradually increase if metrics look good
• Keep rollback plan ready

Monitoring and Alerting

Don't wait for users to report issues. Set up proactive monitoring:

Model Performance Monitoring

• Prediction Drift - Distribution of predictions changing over time
• Feature Drift - Input distributions shifting
• Performance Metrics - Accuracy, precision, recall (when ground truth available)

Infrastructure Monitoring

• Endpoint latency (P50, P95, P99)
• Error rates and failure patterns
• Resource utilization (CPU, memory, GPU)
• Cost tracking per model

Alert Thresholds

Set alerts for:

• Latency exceeds SLA thresholds
• Error rate > 1%
• Prediction drift score > defined threshold
• Daily inference volume drops > 50%

Cost Optimization Tips

Vertex AI costs can add up. Here's how to keep them reasonable:

1. Right-size machine types - Start small, scale up only if needed
2. Use accelerators wisely - Not every model needs a GPU
3. Batch predictions - Use preemptible instances when possible
4. Autoscaling - Set max instances to control costs
5. Regional selection - Some regions are cheaper than others
6. Model optimization - Quantization and distillation reduce serving costs

CI/CD Integration

Integrate MLOps with your existing DevOps workflows:

• Cloud Build - Trigger pipeline builds on code commits
• Artifact Registry - Version control for container images
• Secret Manager - Secure credential management
• Cloud Deploy - Promote models through environments

Common Gotchas to Avoid

After several implementations, these are the issues I see repeatedly:

1. Not testing pipelines end-to-end - Small dataset smoke tests save debugging time
2. Hardcoded paths and configs - Use parameterized pipelines
3. Ignoring training/serving skew - Ensure preprocessing is identical
4. No rollback strategy - Always keep previous model version deployable
5. Insufficient logging - Log predictions and features for debugging

Conclusion

Vertex AI provides solid building blocks for MLOps, but success requires thoughtful architecture. Start with simple pipelines, automate incrementally, and always monitor in production.

The goal isn't the fanciest MLOps setup—it's reliable, maintainable ML systems that deliver business value.

Working on MLOps at your organisation? I'd love to hear about your challenges. Connect with me on LinkedIn.