Building Production ML Pipelines: Lessons Learned

Over the past year, I've been working on a machine learning pipeline that serves over 10 million predictions daily with sub-50ms latency. Here's what I learned about building production ML systems.

Architecture Overview

The pipeline consists of several key components:

1. Training Pipeline: Automated retraining on fresh data
2. Model Registry: Versioning and artifact management with MLflow
3. Serving Layer: FastAPI + ONNX Runtime for low-latency inference
4. Monitoring: Custom metrics for model drift and performance

Key Lessons

1. Start with Simple Baselines

Don't jump straight to complex models. A simple linear model or decision tree can often provide surprising performance and serves as a solid baseline.

from sklearn.linear_model import LogisticRegression

baseline_model = LogisticRegression(max_iter=1000)
baseline_model.fit(X_train, y_train)
baseline_score = baseline_model.score(X_test, y_test)

2. Monitoring is Non-Negotiable

Production ML isn't just about accuracy—it's about reliability. Monitor:

• Input feature distributions (detect drift)
• Prediction distributions
• Model latency (p50, p95, p99)
• Business metrics

3. Version Everything

Models, data, code, dependencies—version it all. We use:

• Git for code
• DVC for data versioning
• MLflow for model artifacts
• Docker for reproducible environments

Performance Optimizations

Moving from PyTorch to ONNX Runtime reduced our inference latency by 60%:

import onnxruntime as ort

session = ort.InferenceSession("model.onnx")
outputs = session.run(None, {"input": input_data})

Conclusion

Building production ML systems requires thinking beyond just model accuracy. Focus on reliability, monitoring, and maintainability from day one.

The full architecture and code examples are available in my GitHub repository.