MLOps: Best Practices for Production AI

Introduction

Deploying a model to a notebook is easy; maintaining it in production is hard. MLOps (Machine Learning Operations) applies DevOps principles to ML systems to ensure reliability and scalability.

The MLOps Lifecycle

1. Data Management

• Versioning: Tools like DVC allow you to version control your datasets alongside your code.
• Validation: Automated checks to ensure data quality before training.

2. Continuous Training (CT)

Models degrade over time (concept drift). Automated pipelines should retrain models as new data arrives.

pipeline:
  stages:
    - data_ingestion
    - training
    - evaluation
    - registration

3. Model Deployment

• Canary Deployment: Roll out models to a small subset of users first.
• A/B Testing: Compare new models against the current baseline in production.

4. Monitoring

It's not enough to monitor latency and errors. You must monitor model performance:

• Prediction drift
• Data drift
• Accuracy metrics

Tooling Ecosystem

• Tracking: MLflow, Weights & Biases
• Orchestration: Kubeflow, Airflow
• Serving: TensorFlow Serving, TorchServe, KServe

Conclusion

Effective MLOps is the difference between a proof-of-concept and a business-critical AI system. It enables teams to iterate faster and deploy with confidence.

Avrut Solutions offers end-to-end MLOps consulting to help you streamline your AI delivery pipeline.