ML Model Deployment Best Practices
Getting a machine learning model into production is often the hardest part of any ML project. Here's what we've learned from deploying hundreds of models.
The Deployment Challenge
Most data science teams can build great models in notebooks. But production deployment requires:
- Version control for models
- Reproducible training pipelines
- Scalable inference infrastructure
- Monitoring and alerting
- A/B testing capabilities
Our MLOps Stack
1. Model Registry
We use MLflow to track experiments and version models. Every model has complete lineage—training data, hyperparameters, and performance metrics.
2. Containerization
All models are containerized with Docker, ensuring consistent behavior across development and production. We package the model, dependencies, and inference code together.
3. Serving Infrastructure
For real-time inference:
- TensorFlow Serving or TorchServe for deep learning
- FastAPI for custom model APIs
- Kubernetes for orchestration and scaling
4. Monitoring
We track:
- Prediction latency and throughput
- Model accuracy over time (drift detection)
- Input data distribution changes
- Resource utilization
Key Lessons Learned
1. Start monitoring early - Don't wait for problems 2. Plan for model updates - You'll retrain more often than you think 3. Cache predictions - Many requests are repeated 4. Have a fallback - When the model fails, what happens?
