Production in Machine Learning
Getting a machine learning model into production — where it actively serves real predictions to real users — introduces a whole new set of engineering challenges beyond model accuracy.
Deployment Approaches
| Approach | Description |
|---|---|
| Batch Inference | Predictions generated periodically for a set of records |
| Real-Time (Online) Inference | Predictions generated on-demand via an API |
| Edge Deployment | Model runs directly on a device (phone, IoT sensor) |
Key Production Concerns
- Latency: How quickly the model must respond to a request.
- Scalability: Handling variable and growing request volume.
- Model Versioning: Tracking which model version is live and enabling rollback.
- Monitoring: Watching for performance degradation or data drift over time.
- Data/Concept Drift: Real-world data changes over time, which can silently reduce model accuracy.
MLOps Practices
- CI/CD for ML: Automated testing and deployment pipelines for models.
- Feature Stores: Centralized, consistent feature computation for training and serving.
- Model Registries: Centralized tracking of model versions and metadata.
- Automated Retraining: Scheduled or trigger-based retraining as new data arrives.
Common Production Tools
- Docker & Kubernetes: Containerizing and orchestrating model services.
- MLflow: Experiment tracking and model registry.
- TensorFlow Serving / TorchServe: Serving frameworks for deep learning models.
Key Takeaway: A model that never reaches production delivers zero business value — the engineering discipline of shipping and maintaining models is as important as building them.
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