Machine Learning Pipeline Basics for Software Developers
From raw data to a served model: training, validation, packaging, and monitoring—framed for engineers who ship services.
Data & machine learning Intermediate 7 min read
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ML pipelines exist because training is non-deterministic without controlled data and seeds, and serving is just another service with SLAs. You version data and models so auditors—and future you—can reproduce a score or debug drift.
Stages
Data
Why version datasets: If metrics move, you must know whether data changed, code changed, or both.
Training
Why log hyperparameters: Otherwise you cannot reproduce the winning run or compare experiments fairly.
Evaluation
Why multiple metrics: Accuracy hides class imbalance; pick metrics aligned with business harm.
Packaging
Why standard formats: Serving runtimes (TensorRT, ONNX, JVM) consume known artifacts without recompiling training code.
Deployment
See REST basics for HTTP interfaces.
Monitoring
Why monitor inputs: Production distributions drift; silent degradation happens before accuracy metrics move.
Developer mindset
ML is software with statistical failure modes—feature flags, rollbacks, and shadow traffic help.
Frequently asked questions
Do I need Kubernetes?
Not on day one; a container + load balancer often suffices until traffic grows.
Where does Python fit?
Training is often Python; serving may be Python, Java, Go, or specialized runtimes.
Feature stores?
Useful when many teams share features online and offline—adopt when duplication hurts.
MLOps vs DevOps?
MLOps adds experiment tracking, model registry, and data validation to classic DevOps.