Machine Learning in Production: From Model to Deployment

Machine Learning05/03/2026

Beyond the Notebook

Building a model that works in a Jupyter notebook is only 20% of the ML lifecycle. Production ML requires robust data pipelines, model versioning, monitoring, and continuous retraining — all while maintaining low latency and high availability.

The MLOps Stack

  • Data versioning: DVC or LakeFS for reproducible datasets.
  • Experiment tracking: MLflow, Weights & Biases, or Neptune for comparing runs.
  • Model registry: Centralized storage for approved model versions.
  • Serving: TensorFlow Serving, TorchServe, or ONNX Runtime for inference.
  • Monitoring: Track data drift, prediction drift, and model performance degradation.

Common Pitfalls

Training-serving skew, silent data drift, and unmonitored model decay are the top reasons ML projects fail in production. Invest in observability from day one and establish clear rollback procedures.

When to Use ML vs LLMs

Traditional ML excels at structured prediction (churn, fraud, demand forecasting). LLMs excel at unstructured language tasks. Many modern products combine both — ML for scoring and routing, LLMs for generation and reasoning.