PART 5 · MODULE 3DevOps for machine learning
MLOps Basics
Operational practices for ML systems
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Operational practices for ML systems
In traditional software, you write code, test it, and deploy it. With ML, you also have to manage data, models, and experiments. MLOps is the set of practices that makes ML development as reliable and repeatable as regular software engineering.
Regular software breaks when the code changes. ML systems can break even when the code stays the same -- because the data changes.
MLOps Pipeline — Automated and Repeatable
Not just code. Track your datasets, model weights, and configuration files. When something breaks, you need to know exactly what changed.
Automated pipelines that retrain your model when new data arrives, run evaluation tests, and deploy if the new model passes quality checks.
Record every training run -- the hyperparameters you tried, the metrics you got, the data you used. Without this, you cannot reproduce your best results.
A catalog of all your trained models with metadata: who trained it, when, on what data, and how well it performed. Like a library catalog for models.
A centralized place to define, store, and serve the input features your models need. Ensures training and serving use the exact same data transformations.
The ability to recreate any past result. If a model worked great three months ago and now it does not, you need to go back and figure out exactly what was different.
Experiment tracking & model registry
Experiment visualization & collaboration
ML pipelines on Kubernetes
Version control for data & models
MLOps is about making ML development as reliable and repeatable as software engineering. Without it, data science teams spend more time debugging infrastructure than improving models. The teams that invest in MLOps ship better models faster.